Journal articles on the topic 'Deep Generatve Models'
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Mehmood, Rayeesa, Rumaan Bashir, and Kaiser J. Giri. "Deep Generative Models: A Review." Indian Journal Of Science And Technology 16, no. 7 (February 21, 2023): 460–67. http://dx.doi.org/10.17485/ijst/v16i7.2296.
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Ragoza, Matthew, Tomohide Masuda, and David Ryan Koes. "Generating 3D molecules conditional on receptor binding sites with deep generative models." Chemical Science 13, no. 9 (2022): 2701–13. http://dx.doi.org/10.1039/d1sc05976a.
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We generate 3D molecules conditioned on receptor binding sites by training a deep generative model on protein–ligand complexes. Our model uses the conditional receptor information to make chemically relevant changes to the generated molecules.
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Salakhutdinov, Ruslan. "Learning Deep Generative Models." Annual Review of Statistics and Its Application 2, no. 1 (April 10, 2015): 361–85. http://dx.doi.org/10.1146/annurev-statistics-010814-020120.
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Partaourides, Harris, and Sotirios P. Chatzis. "Asymmetric deep generative models." Neurocomputing 241 (June 2017): 90–96. http://dx.doi.org/10.1016/j.neucom.2017.02.028.
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Changsheng Du, Changsheng Du, Yong Li Changsheng Du, and Ming Wen Yong Li. "G-DCS: GCN-Based Deep Code Summary Generation Model." 網際網路技術學刊 24, no. 4 (July 2023): 965–73. http://dx.doi.org/10.53106/160792642023072404014.
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<p>In software engineering, software personnel faced many large-scale software and complex systems, these need programmers to quickly and accurately read and understand the code, and efficiently complete the tasks of software change or maintenance tasks. Code-NN is the first model to use deep learning to accomplish the task of code summary generation, but it is not used the structural information in the code itself. In the past five years, researchers have designed different code summarization systems based on neural networks. They generally use the end-to-end neural machine translation framework, but many current research methods do not make full use of the structural information of the code. This paper raises a new model called G-DCS to automatically generate a summary of java code; the generated summary is designed to help programmers quickly comprehend the effect of java methods. G-DCS uses natural language processing technology, and training the model uses a code corpus. This model could generate code summaries directly from the code files in the coded corpus. Compared with the traditional method, it uses the information of structural on the code. Through Graph Convolutional Neural Network (GCN) extracts the structural information on the code to generate the code sequence, which makes the generated code summary more accurate. The corpus used for training was obtained from GitHub. Evaluation criteria using BLEU-n. Experimental results show that our approach outperforms models that do not utilize code structure information.</p> <p> </p>
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Wu, Han. "Face image generation and feature visualization using deep convolutional generative adversarial networks." Journal of Physics: Conference Series 2634, no. 1 (November 1, 2023): 012041. http://dx.doi.org/10.1088/1742-6596/2634/1/012041.
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Abstract Generative Neural Networks (GAN) aims to generate realistic and recognizable images, including portraits, cartoons and other modalities. Image generation has broad application prospects and important research value in the fields of public security and digital entertainment, and has become one of the current research hotspots. This article will introduce and apply an important image generation model called GAN, which stands for Generative Adversarial Network. Unlike recent image processing models such as Variational Autoencoders (VAE), The discriminative network evaluates potential candidates while the GAN generates candidates. As a result, the discriminative network distinguishes created and real candidates, while the generative network learns to map from a latent space to an interest data distribution. In this article, the GAN model and some of its extensions will be thoroughly applied and implemented based on the dataset of CelebA, and details will be discussed through the images and graphs generated by the model. Specific training methods for various models and optimization algorithms can be produced by the GAN framework. The experiment’s findings in this article will show how the framework’s potential may be quantified and qualitatively assessed using the samples that were produced.
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Berrahal, Mohammed, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari, and Idriss Idrissi. "Investigating the effectiveness of deep learning approaches for deep fake detection." Bulletin of Electrical Engineering and Informatics 12, no. 6 (December 1, 2023): 3853–60. http://dx.doi.org/10.11591/eei.v12i6.6221.
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As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This paper aims to provide an in-depth discussion about the challenge of generated fake face image detection. We explain the different datasets and the various proposed deep learning models for fake face image detection. The models used were trained on a large dataset of real data from CelebA-HQ and fake data from a trained generative adversarial network (GAN) based generator. All testing models achieved high accuracy in detecting the fake images, especially residual neural network (ResNet50) which performed the best among with an accuracy of 99.43%.
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Che, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong, and Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.
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AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints to separate the label information from the latent representation. We give both intuitive and theoretical justifications for the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on both out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.
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Scurto, Hugo, Thomas Similowski, Samuel Bianchini, and Baptiste Caramiaux. "Probing Respiratory Care With Generative Deep Learning." Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (September 28, 2023): 1–34. http://dx.doi.org/10.1145/3610099.
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This paper combines design, machine learning and social computing to explore generative deep learning as both tool and probe for respiratory care. We first present GANspire, a deep learning tool that generates fine-grained breathing waveforms, which we crafted in collaboration with one respiratory physician, attending to joint materialities of human breathing data and deep generative models. We then relate a probe, produced with breathing waveforms generated with GANspire, and led with a group of ten respiratory care experts, responding to its material attributes. Qualitative annotations showed that respiratory care experts interpreted both realistic and ambiguous attributes of breathing waveforms generated with GANspire, according to subjective aspects of physiology, activity and emotion. Semi-structured interviews also revealed experts' broader perceptions, expectations and ethical concerns on AI technology, based on their clinical practice of respiratory care, and reflexive analysis of GANspire. These findings suggest design implications for technological aids in respiratory care, and show how ambiguity of deep generative models can be leveraged as a resource for qualitative inquiry, enabling socio-material research with generative deep learning. Our paper contributes to the CSCW community by broadening how generative deep learning may be approached not only as a tool to design human-computer interactions, but also as a probe to provoke open conversations with communities of practice about their current and speculative uses of AI technology.
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Prakash Patil, Et al. "GAN-Enhanced Medical Image Synthesis: Augmenting CXR Data for Disease Diagnosis and Improving Deep Learning Performance." Journal of Electrical Systems 19, no. 3 (January 25, 2024): 53–61. http://dx.doi.org/10.52783/jes.651.
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Deep learning is increasing the need for accurate and reliable medical image analysis tools, especially for CXR disease diagnosis. This study proposes the Attention Mechanisms based Cycle-Consistent GAN (AM-CGAN) to address the lack of annotated medical data. To produce realistic and clinically relevant CXR images, our model uses Generative Adversarial Networks (GAN) and attention mechanisms. Downstream deep learning models for disease classification improve with this enhancement. The Attention Mechanisms based Cycle-Consistent GAN (AM-CGAN) improves the accuracy and reliability of deep learning models used for medical image analysis, specifically for Chest X-ray (CXR) data. CXR data is enhanced to improve disease diagnosis. Generative Adversarial Networks (GAN) create realistic medical images in the proposed model. It also uses attention mechanisms to highlight key areas in generated images. This research aims to address the lack of annotated medical data, particularly for CXR images. Training deep learning models is difficult due to the lack of diverse and well-annotated datasets. Our proposed AM-CGAN uses attention mechanisms to generate synthetic CXR images that closely resemble medical images and highlight disease-specific characteristics. AM-CGAN uses Cycle-Consistent GAN to ensure that generated images match the input distribution and prevent mode collapse. While synthesizing images, the model can selectively focus on important anatomical structures and pathological indicators using attention mechanisms. This attention-driven approach improves the clinical significance of generated images, making them better for training accurate and reliable disease classification models. Many experiments were done to test the AM-CGAN on CXR images of COVID-19, pneumonia, and normal cases. The quantitative results show high precision (98.15% accuracy). This shows the model's ability to create medical-data-like synthetic images. Downstream deep learning models trained on the augmented dataset perform better at capturing disease-specific characteristics. This study advances GAN-enhanced medical image synthesis research and addresses the data shortage in medical imaging research. The AM-CGAN attention-driven focus on disease-related regions in CXR data suggests a promising way to improve diagnostic models, especially in situations with few labeled datasets. The AM-CGAN bridges the gap between diverse data and sophisticated deep learning models for disease diagnosis, making it a major advancement in medical image analysis.
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Cui, Bo, Guyue Hu, and Shan Yu. "DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1175–83. http://dx.doi.org/10.1609/aaai.v35i2.16204.
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An important challenge for neural networks is to learn incrementally, i.e., learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usually suffer from increased distribution mismatch between the generated and original samples along the learning process. In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. We develop a discriminative learning model to incrementally update the latent feature space for continual classification. At the same time, a generative model is introduced to achieve conditional generation using the latent feature distribution produced by the discriminative model. Importantly, the generative and discriminative models are connected through bidirectional training to enforce cycle-consistency of mappings between feature and image domains. Furthermore, a domain alignment module is used to eliminate the divergence between the feature distributions of generated images and real ones. This module together with the discriminative model can perform effective sample mining to facilitate incremental learning. Extensive experiments on several visual recognition datasets show that our system can achieve state-of-the-art performance.
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Peng, Shi-Ping, Xin-Yu Yang, and Yi Zhao. "Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning." International Journal of Molecular Sciences 22, no. 16 (August 23, 2021): 9099. http://dx.doi.org/10.3390/ijms22169099.
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The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model.
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Zeng, Jinshan, Qi Chen, Yunxin Liu, Mingwen Wang, and Yuan Yao. "StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3270–77. http://dx.doi.org/10.1609/aaai.v35i4.16438.
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The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particularly, the generative adversarial networks (GAN) based models. However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results. In this paper, we introduce a one-bit stroke encoding to capture the key mode information of Chinese characters and then incorporate it into CycleGAN, a popular deep generative model for Chinese font generation. As a result we propose an efficient method called StrokeGAN, mainly motivated by the observation that the stroke encoding contains amount of mode information of Chinese characters. In order to reconstruct the one-bit stroke encoding of the associated generated characters, we introduce a stroke-encoding reconstruction loss imposed on the discriminator. Equipped with such one-bit stroke encoding and stroke-encoding reconstruction loss, the mode collapse issue of CycleGAN can be significantly alleviated, with an improved preservation of strokes and diversity of generated characters. The effectiveness of StrokeGAN is demonstrated by a series of generation tasks over nine datasets with different fonts. The numerical results demonstrate that StrokeGAN generally outperforms the state-of-the-art methods in terms of content and recognition accuracies, as well as certain stroke error, and also generates more realistic characters.
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Qiang, Zhenping, Libo He, Qinghui Zhang, and Junqiu Li. "Face Inpainting with Deep Generative Models." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 1232. http://dx.doi.org/10.2991/ijcis.d.191016.003.
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Du, Fang, Jiangshe Zhang, Junying Hu, and Rongrong Fei. "Discriminative multi-modal deep generative models." Knowledge-Based Systems 173 (June 2019): 74–82. http://dx.doi.org/10.1016/j.knosys.2019.02.023.
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Xu, Jungang, Hui Li, and Shilong Zhou. "An Overview of Deep Generative Models." IETE Technical Review 32, no. 2 (December 20, 2014): 131–39. http://dx.doi.org/10.1080/02564602.2014.987328.
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Jørgensen, Peter B., Mikkel N. Schmidt, and Ole Winther. "Deep Generative Models for Molecular Science." Molecular Informatics 37, no. 1-2 (January 2018): 1700133. http://dx.doi.org/10.1002/minf.201700133.
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Ahmad, Bilal, Jun Sun, Qi You, Vasile Palade, and Zhongjie Mao. "Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks." Biomedicines 10, no. 2 (January 21, 2022): 223. http://dx.doi.org/10.3390/biomedicines10020223.
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Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.
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Andreu, Sergi, and Monica Villanueva Aylagas. "Neural Synthesis of Sound Effects Using Flow-Based Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 18, no. 1 (October 11, 2022): 2–9. http://dx.doi.org/10.1609/aiide.v18i1.21941.
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Creating variations of sound effects for video games is a time-consuming task that grows with the size and complexity of the games themselves. The process usually comprises recording source material and mixing different layers of sound to create sound effects that are perceived as diverse during gameplay. In this work, we present a method to generate controllable variations of sound effects that can be used in the creative process of sound designers. We adopt WaveFlow, a generative flow model that works directly on raw audio and has proven to perform well for speech synthesis. Using a lower-dimensional mel spectrogram as the conditioner allows both user controllability and a way for the network to generate more diversity. Additionally, it gives the model style transfer capabilities. We evaluate several models in terms of the quality and variability of the generated sounds using both quantitative and subjective evaluations. The results suggest that there is a trade-off between quality and diversity. Nevertheless, our method achieves a quality level similar to that of the training set while generating perceivable variations according to a perceptual study that includes game audio experts.
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Karimi, Mostafa, Arman Hasanzadeh, and Yang Shen. "Network-principled deep generative models for designing drug combinations as graph sets." Bioinformatics 36, Supplement_1 (July 1, 2020): i445—i454. http://dx.doi.org/10.1093/bioinformatics/btaa317.
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Abstract Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information Supplementary data are available at Bioinformatics online.
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Hawkins-Hooker, Alex, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, and David Bikard. "Generating functional protein variants with variational autoencoders." PLOS Computational Biology 17, no. 2 (February 26, 2021): e1008736. http://dx.doi.org/10.1371/journal.pcbi.1008736.
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The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequence data have been shown to learn biologically meaningful representations helpful for a variety of downstream tasks, but their potential for direct use in the design of novel proteins remains largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases can be used to generate novel, functional variants of the luxA bacterial luciferase. We propose separate VAE models to work with aligned sequence input (MSA VAE) and raw sequence input (AR-VAE), and offer evidence that while both are able to reproduce patterns of amino acid usage characteristic of the family, the MSA VAE is better able to capture long-distance dependencies reflecting the influence of 3D structure. To confirm the practical utility of the models, we used them to generate variants of luxA whose luminescence activity was validated experimentally. We further showed that conditional variants of both models could be used to increase the solubility of luxA without disrupting function. Altogether 6/12 of the variants generated using the unconditional AR-VAE and 9/11 generated using the unconditional MSA VAE retained measurable luminescence, together with all 23 of the less distant variants generated by conditional versions of the models; the most distant functional variant contained 35 differences relative to the nearest training set sequence. These results demonstrate the feasibility of using deep generative models to explore the space of possible protein sequences and generate useful variants, providing a method complementary to rational design and directed evolution approaches.
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Hess, Moritz, Maren Hackenberg, and Harald Binder. "Exploring generative deep learning for omics data using log-linear models." Bioinformatics 36, no. 20 (August 1, 2020): 5045–53. http://dx.doi.org/10.1093/bioinformatics/btaa623.
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Abstract Motivation Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications. Results We demonstrate how log-linear models, fitted to the generated, synthetic data can be used to extract patterns from omics data, learned by deep generative techniques. Specifically, interactions between latent representations learned by the approaches and generated synthetic data are used to determine sets of joint patterns. Distances of patterns with respect to the distribution of latent representations are then visualized in low-dimensional coordinate systems, e.g. for monitoring training progress. This is illustrated with simulated data and subsequently with cortical single-cell gene expression data. Using different kinds of deep generative techniques, specifically variational autoencoders and deep Boltzmann machines, the proposed approach highlights how the techniques uncover underlying structure. It facilitates the real-world use of such generative deep learning techniques to gain biological insights from omics data. Availability and implementation The code for the approach as well as an accompanying Jupyter notebook, which illustrates the application of our approach, is available via the GitHub repository: https://github.com/ssehztirom/Exploring-generative-deep-learning-for-omics-data-by-using-log-linear-models. Supplementary information Supplementary data are available at Bioinformatics online.
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Tang, Keke, Jianpeng Wu, Weilong Peng, Yawen Shi, Peng Song, Zhaoquan Gu, Zhihong Tian, and Wenping Wang. "Deep Manifold Attack on Point Clouds via Parameter Plane Stretching." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2420–28. http://dx.doi.org/10.1609/aaai.v37i2.25338.
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Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.
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He, Yu, Shuai Li, Xin Wen, and Jing Xu. "A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection." Sensors 24, no. 8 (April 20, 2024): 2642. http://dx.doi.org/10.3390/s24082642.
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Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance.
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Samanta, Bidisha, Abir DE, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez Rodriguez. "NeVAE: A Deep Generative Model for Molecular Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1110–17. http://dx.doi.org/10.1609/aaai.v33i01.33011110.
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Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics—their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we propose NeVAE, a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. In addition, by using masking, the decoder is able to guarantee a set of valid properties in the generated molecules. Experiments reveal that our model can discover plausible, diverse and novel molecules more effectively than several state of the art methods. Moreover, by utilizing Bayesian optimization over the continuous latent representation of molecules our model finds, we can also find molecules that maximize certain desirable properties more effectively than alternatives.
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Yue, Yunpeng, Hai Liu, Xu Meng, Yinguang Li, and Yanliang Du. "Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks." Remote Sensing 13, no. 22 (November 15, 2021): 4590. http://dx.doi.org/10.3390/rs13224590.
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Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.
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He, Junpeng, Lei Luo, Kun Xiao, Xiyu Fang, and Yun Li. "Generate qualified adversarial attacks and foster enhanced models based on generative adversarial networks." Intelligent Data Analysis 26, no. 5 (September 5, 2022): 1359–77. http://dx.doi.org/10.3233/ida-216134.
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In cybersecurity, intrusion detection systems (IDSes) are of vital importance, allowing different companies and their departments to identify malicious attacks from magnanimous network traffic; however, the effectiveness and stability of these artificial intelligence-based systems are challenged when coping with adversarial attacks. This work explores a creative framework based on a generative adversarial network (GAN) with a series of training algorithms that aims to generate instances of adversarial attacks and utilize them to help establish a new IDS based on a neural network that can replace the old IDS without knowledge of any of its parameters. Furthermore, to verify the quality of the generated attacks, a transfer mechanism is proposed for calculating the Frechet inception distance (FID). Experiments show that based on the original CICIDS2017 dataset, the proposed framework can generate four types of adversarial attacks (DDoS, DoS, Bruteforce, and Infiltration), which precipitate four types of classifiers (Decision Tree, Random Forest, Adaboost, and Deep Neural Network), set as black-box old IDSes, with low detection rates; additionally, the IDSes that the proposed framework newly establish have an average detection rate of 98% in coping with both generated adversarial and original attacks.
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Liu, Yukai. "Data augmentation-based enhanced fingerprint recognition using deep convolutional generative adversarial network and diffusion models." Applied and Computational Engineering 52, no. 1 (March 27, 2024): 8–13. http://dx.doi.org/10.54254/2755-2721/52/20241115.
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The progress of fingerprint recognition applications encounters substantial hurdles due to privacy and security concerns, leading to limited fingerprint data availability and stringent data quality requirements. This article endeavors to tackle the challenges of data scarcity and data quality in fingerprint recognition by implementing data augmentation techniques. Specifically, this research employed two state-of-the-art generative models in the domain of deep learning, namely Deep Convolutional Generative Adversarial Network (DCGAN) and the Diffusion model, for fingerprint data augmentation. Generative Adversarial Network (GAN), as a popular generative model, effectively captures the features of sample images and learns the diversity of the sample images, thereby generating realistic and diverse images. DCGAN, as a variant model of traditional GAN, inherits the advantages of GAN while alleviating issues such as blurry images and mode collapse, resulting in improved performance. On the other hand, Diffusion, as one of the most popular generative models in recent years, exhibits outstanding image generation capabilities and surpasses traditional GAN in some image generation tasks. The experimental results demonstrate that both DCGAN and Diffusion can generate clear, high-quality fingerprint images, fulfilling the requirements of fingerprint data augmentation. Furthermore, through the comparison between DCGAN and Diffusion, it is concluded that the quality of fingerprint images generated by DCGAN is superior to the results of Diffusion, and DCGAN exhibits higher efficiency in both training and generating images compared to Diffusion.
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Lanusse, François, Rachel Mandelbaum, Siamak Ravanbakhsh, Chun-Liang Li, Peter Freeman, and Barnabás Póczos. "Deep generative models for galaxy image simulations." Monthly Notices of the Royal Astronomical Society 504, no. 4 (May 4, 2021): 5543–55. http://dx.doi.org/10.1093/mnras/stab1214.
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ABSTRACT Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available space-based data. In this work, we propose a methodology based on deep generative models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and point spread function (PSF)-convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the PSF and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second- and higher order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce galsim-hub, a community-driven repository of generative models, and a framework for incorporating generative models within the galsim image simulation software.
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Wu, Zachary, Kadina E. Johnston, Frances H. Arnold, and Kevin K. Yang. "Protein sequence design with deep generative models." Current Opinion in Chemical Biology 65 (December 2021): 18–27. http://dx.doi.org/10.1016/j.cbpa.2021.04.004.
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Sensoy, Murat, Lance Kaplan, Federico Cerutti, and Maryam Saleki. "Uncertainty-Aware Deep Classifiers Using Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5620–27. http://dx.doi.org/10.1609/aaai.v34i04.6015.
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Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.
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Bejarano, Gissella, David DeFazio, and Arti Ramesh. "Deep Latent Generative Models for Energy Disaggregation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 850–57. http://dx.doi.org/10.1609/aaai.v33i01.3301850.
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Thoroughly understanding how energy consumption is disaggregated into individual appliances can help reduce household expenses, integrate renewable sources of energy, and lead to efficient use of energy. In this work, we propose a deep latent generative model based on variational recurrent neural networks (VRNNs) for energy disaggregation. Our model jointly disaggregates the aggregated energy signal into individual appliance signals, achieving superior performance when compared to the state-of-the-art models for energy disaggregation, yielding a 29% and 41% performance improvement on two energy datasets, respectively, without explicitly encoding temporal/contextual information or heuristics. Our model also achieves better prediction performance on lowpower appliances, paving the way for a more nuanced disaggregation model. The structured output prediction in our model helps in accurately discerning which appliance(s) contribute to the aggregated power consumption, thus providing a more useful and meaningful disaggregation model.
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Kalibhat, Neha Mukund, Yogesh Balaji, and Soheil Feizi. "Winning Lottery Tickets in Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8038–46. http://dx.doi.org/10.1609/aaai.v35i9.16980.
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The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network. Prior works in lottery tickets have primarily focused on the supervised learning setup, with several papers proposing effective ways of finding winning tickets in classification problems. In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs. We show that the popular iterative magnitude pruning approach (with late resetting) can be used with generative losses to find the winning tickets. This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets. We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models. Furthermore, we show the practical benefits of lottery tickets in generative models by detecting tickets at very early stages in training called early-bird tickets. Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints. These results out-perform existing early pruning methods like SNIP (Lee, Ajanthan, and Torr 2019) and GraSP(Wang, Zhang, and Grosse 2020). Our findings shed light towards existence of proper network initializations that could improve convergence and stability of generative models.
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Ojeda, Cesar, Kostadin Cvejoski, Bodgan Georgiev, Christian Bauckhage, Jannis Schuecker, and Ramses J. Sanchez. "Learning Deep Generative Models for Queuing Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9214–22. http://dx.doi.org/10.1609/aaai.v35i10.17112.
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Modern society is heavily dependent on large scale client-server systems with applications ranging from Internet and Communication Services to sophisticated logistics and deployment of goods. To maintain and improve such a system, a careful study of client and server dynamics is needed – e.g. response/service times, aver-age number of clients at given times, etc. To this end, one traditionally relies, within the queuing theory formalism,on parametric analysis and explicit distribution forms.However, parametric forms limit the model’s expressiveness and could struggle on extensively large datasets. We propose a novel data-driven approach towards queuing systems: the Deep Generative Service Times. Our methodology delivers a flexible and scalable model for service and response times. We leverage the representation capabilities of Recurrent Marked Point Processes for the temporal dynamics of clients, as well as Wasserstein Generative Adversarial Network techniques, to learn deep generative models which are able to represent complex conditional service time distributions. We provide extensive experimental analysis on both empirical and synthetic datasets, showing the effectiveness of the proposed models
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Berns, Sebastian. "Increasing the Diversity of Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12870–71. http://dx.doi.org/10.1609/aaai.v36i11.21572.
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Generative models are used in a variety of applications that require diverse output. Yet, models are primarily optimised for sample fidelity and mode coverage. My work aims to increase the output diversity of generative models for multi-solution tasks. Previously, we analysed the use of generative models in artistic settings and how its objective diverges from distribution fitting. For specific use cases, we quantified the limitations of generative models. Future work will focus on adapting generative modelling for downstream tasks that require a diverse set of high-quality artefacts.
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Suzuki, Masahiro, and Yutaka Matsuo. "A survey of multimodal deep generative models." Advanced Robotics 36, no. 5-6 (February 21, 2022): 261–78. http://dx.doi.org/10.1080/01691864.2022.2035253.
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Kang, Seokho, and Kyunghyun Cho. "Conditional Molecular Design with Deep Generative Models." Journal of Chemical Information and Modeling 59, no. 1 (July 17, 2018): 43–52. http://dx.doi.org/10.1021/acs.jcim.8b00263.
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Imrie, Fergus, Anthony R. Bradley, Mihaela van der Schaar, and Charlotte M. Deane. "Deep Generative Models for 3D Linker Design." Journal of Chemical Information and Modeling 60, no. 4 (March 20, 2020): 1983–95. http://dx.doi.org/10.1021/acs.jcim.9b01120.
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Besedin, Andrey, Pierre Blanchart, Michel Crucianu, and Marin Ferecatu. "Deep online classification using pseudo-generative models." Computer Vision and Image Understanding 201 (December 2020): 103048. http://dx.doi.org/10.1016/j.cviu.2020.103048.
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Baillif, Benoit, Jason Cole, Patrick McCabe, and Andreas Bender. "Deep generative models for 3D molecular structure." Current Opinion in Structural Biology 80 (June 2023): 102566. http://dx.doi.org/10.1016/j.sbi.2023.102566.
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Behnia, Farnaz, Dominik Karbowski, and Vadim Sokolov. "Deep generative models for vehicle speed trajectories." Applied Stochastic Models in Business and Industry 39, no. 5 (September 2023): 701–19. http://dx.doi.org/10.1002/asmb.2816.
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AbstractGenerating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self‐driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed‐forward layers and are trained using adversarial techniques. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.
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Jung, Steffen, and Margret Keuper. "Spectral Distribution Aware Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1734–42. http://dx.doi.org/10.1609/aaai.v35i2.16267.
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Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actually easily distinguishable by artifacts in their frequency spectra. In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. The proposed discriminator is lightweight, modular and works stably with different commonly used GAN losses. We show that the resulting models can better generate images with realistic frequency spectra, which are thus harder to detect by this cue.
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Bazarbaev, Manas, Tserenpurev Chuluunsaikhan, Hyoseok Oh, Ga-Ae Ryu, Aziz Nasridinov, and Kwan-Hee Yoo. "Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network." Sensors 22, no. 1 (December 22, 2021): 29. http://dx.doi.org/10.3390/s22010029.
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Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.
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Nye, Logan, Hamid Ghaednia, and Joseph H. Schwab. "Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e13592-e13592. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13592.
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e13592 Background: The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. However, creating digital pathology algorithms requires large volumes of training data, often on the order of thousands of histopathology slides. This becomes problematic for rare diseases, where imaging datasets of such size do not exist. This makes it impossible to train digital pathology models for these rare conditions. However, recent advances in generative deep learning models may provide a method for overcoming this lack of histology data for rare diseases. Pre-trained diffusion-based probabilistic models can be used to create photorealistic variations of existing images. In this study, we explored the potential of using a deep generative model created by OpenAI for the purpose of producing synthetic histopathology images, using chondrosarcoma as our rare tumor of interest. Methods: Our team compiled a dataset of 55 chondrosarcoma histolopathology images from the annotated records of Dr. Henry Jaffe, a pioneering authority in musculoskeletal pathology. We built a deep learning image-generation application in a Jupyter notebook environment, iterating upon OpenAI’s DALL-E application processing interface (API) with python programming language. Using the chondrosarcoma histology dataset and NVIDIA GPUs, we trained the deep learning application to generate multiple synthetic variations of each real chondrosarcoma image. Results: After several hours, the deep learning model successfully generated 1,000 images of chondrosarcoma from 55 original images. The synthetic histology images retained photorealistic quality and displayed characteristic cellular features of chondrosarcoma tumor tissue. Conclusions: Deep generative models may be useful in addressing issues of data scarcity in rare diseases, such as chondrosarcoma. For example, in situations where existing imaging data is insufficient for training diagnostic computer vision models, diffusion-based generative models could be applied to create training datasets. However, further exploration of ethical considerations and qualitative analyses of these generated data are needed.
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Du, Chuan, and Lei Zhang. "Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network." Remote Sensing 13, no. 21 (October 29, 2021): 4358. http://dx.doi.org/10.3390/rs13214358.
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Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system output the intended wrong label predictions by adding small adversarial perturbations to the SAR images. The existing optimization-based adversarial attack methods generate adversarial examples by minimizing the mean-squared reconstruction error, causing smooth target edge and blurry weak scattering centers in SAR images. In this paper, we build a UNet-generative adversarial network (GAN) to refine the generation of the SAR-ATR models’ adversarial examples. The UNet learns the separable features of the targets and generates the adversarial examples of SAR images. The GAN makes the generated adversarial examples approximate to real SAR images (with sharp target edge and explicit weak scattering centers) and improves the generation efficiency. We carry out abundant experiments using the proposed adversarial attack algorithm to fool the SAR-ATR models based on several advanced CNNs, which are trained on the measured SAR images of the ground vehicle targets. The quantitative and qualitative results demonstrate the high-quality adversarial example generation and excellent attack effectiveness and efficiency improvement.
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Rojas-Campos, Adrian, Michael Langguth, Martin Wittenbrink, and Gordon Pipa. "Deep learning models for generation of precipitation maps based on numerical weather prediction." Geoscientific Model Development 16, no. 5 (March 8, 2023): 1467–80. http://dx.doi.org/10.5194/gmd-16-1467-2023.
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Abstract. Numerical weather prediction (NWP) models are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational resources required for its functioning, which limits the spatial and temporal resolution of the outputs. Traditional meteorological techniques to increase the resolution are uniquely based on information from a limited group of interest variables. In this study, we offer an alternative approach to the task where we generate precipitation maps based on the complete set of variables of the NWP to generate high-resolution and short-time precipitation predictions. To achieve this, five different deep learning models were trained and evaluated: a baseline, U-Net, two deconvolution networks and one conditional generative model (Conditional Generative Adversarial Network; CGAN). A total of 20 independent random initializations were performed for each of the models. The predictions were evaluated using skill scores based on mean absolute error (MAE) and linear error in probability space (LEPS), equitable threat score (ETS), critical success index (CSI) and frequency bias after applying several thresholds. The models showed a significant improvement in predicting precipitation, showing the benefits of including the complete information from the NWP. The algorithms doubled the resolution of the predictions and corrected an over-forecast bias from the input information. However, some new models presented new types of bias: U-Net tended to mid-range precipitation events, and the deconvolution models favored low rain events and generated some spatial smoothing. The CGAN offered the highest-quality precipitation forecast, generating realistic outputs and indicating possible future research paths.
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Shchetinin, Eugene Yu. "COMPUTER ALGORITHMS FOR SYNTHETIC IMAGES MODELLING BASED ON DIFFUSION MODELS." SOFT MEASUREMENTS AND COMPUTING 11/2, no. 72 (2023): 48–58. http://dx.doi.org/10.36871/2618-9976.2023.11-2.005.
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Deep neural networks have made significant breakthroughs in the field of medical image analysis. However, due to their high data requirements, small datasets in medical imaging tasks can hinder their capabilities. Synthetic data generation is a promising alternative to augment training datasets and enable medical imaging studies on a larger scale. Recently, deep generative models have attracted the attention of the computer vision community because they allow the generation of photorealistic synthetic images. In this paper, we investigate the potential use of deep generative models and develop computer algorithms to generate highresolution synthetic images. The obtained results of computer modelling confirmed the high efficiency and advantages of diffusion models in image synthesis problems.
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Naman and Sudha Narang, Chaudhary Sarimurrab, Ankita Kesari. "Human Face Generation using Deep Convolution Generative Adversarial Network." January 2021 7, no. 01 (January 29, 2021): 114–20. http://dx.doi.org/10.46501/ijmtst070127.
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The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for generating faces are vast in the field of image processing, entertainment, and other such industries. Our resulting model is successfully able to generate human faces from the given un-labelled data and random noise.
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Akande, Timileyin Opeyemi, Oluwaseyi Omotayo Alabi, and Julianah B. Oyinloye. "A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis." Journal of Computing Theories and Applications 2, no. 2 (March 21, 2024): 148–68. http://dx.doi.org/10.62411/jcta.10125.
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Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.
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Seong, Ju Yong, Seung-min Ji, Dong-hyun Choi, Seungjae Lee, and Sungchul Lee. "Optimizing Generative Adversarial Network (GAN) Models for Non-Pneumatic Tire Design." Applied Sciences 13, no. 19 (September 25, 2023): 10664. http://dx.doi.org/10.3390/app131910664.
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Pneumatic tires are used in diverse industries. However, their design is difficult, as it relies on the knowledge of experienced designers. In this paper, we generate images of non-pneumatic tire designs with patterns based on shapes and lines for different generative adversarial network (GAN) models and test the performance of the models. Using OpenCV, 2000 training images were generated, corresponding to spoke, curve, triangle, and honeycomb non-pneumatic tires. The images created for training were used after removing highly similar images by applying mean squared error (MSE) and structural similarity index (SSIM). To identify the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, deep convolutional generative adversarial network (DCGAN), StarGAN v2, StyleGAN v2-ADA, and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, StarGAN v2, and StyleGAN v2-ADA models distorted the circle shape and did not maintain a consistent pattern, but ProjectedGAN retained consistency in the circle, and the pattern was less distorted than in the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed the best among several techniques when the difference between the generated and actual image distributions was measured.