Relevant bibliographies by topics / CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)'

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Published: 11 September 2023

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Journal articles on the topic "CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)"

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Zhou, Guoqiang, Yi Fan, Jiachen Shi, Yuyuan Lu, and Jun Shen. "Conditional Generative Adversarial Networks for Domain Transfer: A Survey." Applied Sciences 12, no. 16 (August 21, 2022): 8350. http://dx.doi.org/10.3390/app12168350.

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Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how to improve it to achieve better performance and how to evaluate such performance across the variants. Afterward, the main applications of CGAN in domain transfer are presented. Finally, as another major contribution, we also list the current problems and challenges of CGAN.
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Lee, Minhyeok, and Junhee Seok. "Estimation with Uncertainty via Conditional Generative Adversarial Networks." Sensors 21, no. 18 (September 15, 2021): 6194. http://dx.doi.org/10.3390/s21186194.

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Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversarial Network (cGAN) that has been routinely used for conditional sample generation. By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model; moreover, the model is robust against noises since adversarial training is employed. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems, respectively. The proposed framework is applied to stock market data and an image classification task. As a result, the proposed framework shows superior estimation performance, especially on noisy data; moreover, it is demonstrated that the proposed framework can properly estimate the uncertainty of predictions.
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Zhang, Hao, and Wenlei Wang. "Imaging Domain Seismic Denoising Based on Conditional Generative Adversarial Networks (CGANs)." Energies 15, no. 18 (September 8, 2022): 6569. http://dx.doi.org/10.3390/en15186569.

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A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface. More and more complex geology has challenged traditional techniques and resulted in a need for more powerful denoising methodologies. The deep learning technique has shown its effectiveness in many different types of tasks. In this work, we used a conditional generative adversarial network (CGAN), which is a special type of deep neural network, to conduct the seismic image denoising process. We considered the denoising task as an image-to-image translation problem, which transfers a raw seismic image with multiple types of noise into a reflectivity-like image without noise. We used several seismic models with complex geology to train the CGAN. In this experiment, the CGAN’s performance was promising. The trained CGAN could maintain the structure of the image undistorted while suppressing multiple types of noise.
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Zand, Jaleh, and Stephen Roberts. "Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)." Signals 2, no. 3 (September 1, 2021): 559–69. http://dx.doi.org/10.3390/signals2030034.

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Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GANs to time series modeling, including forecasting. In this work, we present the Mixture Density Conditional Generative Adversarial Model (MD-CGAN), with a focus on time series forecasting. We show that our model is capable of estimating a probabilistic posterior distribution over forecasts and that, in comparison to a set of benchmark methods, the MD-CGAN model performs well, particularly in situations where noise is a significant component of the observed time series. Further, by using a Gaussian mixture model as the output distribution, MD-CGAN offers posterior predictions that are non-Gaussian.
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Zhen, Hao, Yucheng Shi, Jidong J. Yang, and Javad Mohammadpour Vehni. "Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification." Applied Computing and Intelligence 3, no. 1 (2022): 13–26. http://dx.doi.org/10.3934/aci.2023002.

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<abstract> <p>Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a <italic>co-supervisor</italic> but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.</p> </abstract>
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Huang, Yubo, and Zhong Xiang. "A Metal Character Enhancement Method based on Conditional Generative Adversarial Networks." Journal of Physics: Conference Series 2284, no. 1 (June 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2284/1/012003.

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Abstract In order to improve the accuracy and stability of metal stamping character (MSC) automatic recognition technology, a metal stamping character enhancement algorithm based on conditional Generative Adversarial Networks (cGAN) is proposed. We identify character regions manually through region labeling and Unsharpen Mask (USM) sharpening algorithm, and make the cGAN learn the most effective loss function in the adversarial training process to guide the generated model and distinguish character features and interference features, so as to achieve contrast enhancement between character and non-character regions. Qualitative and quantitative analyses show that the generated results have satisfactory image quality, and that the maximum character recognition rate of the recognition network ASTER is improved by 11.03%.
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Kyslytsyna, Anastasiia, Kewen Xia, Artem Kislitsyn, Isselmou Abd El Kader, and Youxi Wu. "Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks." Sensors 21, no. 21 (November 8, 2021): 7405. http://dx.doi.org/10.3390/s21217405.

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Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods.
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Link, Patrick, Johannes Bodenstab, Lars Penter, and Steffen Ihlenfeldt. "Metamodeling of a deep drawing process using conditional Generative Adversarial Networks." IOP Conference Series: Materials Science and Engineering 1238, no. 1 (May 1, 2022): 012064. http://dx.doi.org/10.1088/1757-899x/1238/1/012064.

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Abstract Optimization tasks as well as quality predictions for process control require fast responding process metamodels. A common strategy for sheet metal forming is building fast data driven metamodels based on results of Finite Element (FE) process simulations. However, FE simulations with complex material models and large parts with many elements consume extensive computational time. Hence, one major challenge in developing metamodels is to achieve a good prediction precision with limited data, while these predictions still need to be robust against varying input parameters. Therefore, the aim of this study was to evaluate if conditional Generative Adversarial Networks (cGAN) are applicable for predicting results of FE deep drawing simulations, since cGANs could achieve high performance in similar tasks in previous work. This involves investigations of the influence of data required to achieve a defined precision and to predict e.g. wrinkling phenomena. Results show that the cGAN used in this study was able to predict forming results with an averaged absolute deviation of sheet thickness of 0.025 mm, even when using a comparable small amount of data.
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Falahatraftar, Farnoush, Samuel Pierre, and Steven Chamberland. "A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks." Telecom 2, no. 1 (March 18, 2021): 141–54. http://dx.doi.org/10.3390/telecom2010009.

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Heterogeneous Vehicular Network (HetVNET) is a highly dynamic type of network that changes very quickly. Regarding this feature of HetVNETs and the emerging notion of network slicing in 5G technology, we propose a hybrid intelligent Software-Defined Network (SDN) and Network Functions Virtualization (NFV) based architecture. In this paper, we apply Conditional Generative Adversarial Network (CGAN) to augment the information of successful network scenarios that are related to network congestion and dynamicity. The results show that the proposed CGAN can be trained in order to generate valuable data. The generated data are similar to the real data and they can be used in blueprints of HetVNET slices.
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Aida, Saori, Junpei Okugawa, Serena Fujisaka, Tomonari Kasai, Hiroyuki Kameda, and Tomoyasu Sugiyama. "Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks." Biomolecules 10, no. 6 (June 19, 2020): 931. http://dx.doi.org/10.3390/biom10060931.

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Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.
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Dissertations / Theses on the topic "CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)"

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Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

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Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen great success in generating complex high-dimensional data, but less work has been done on their use for regression problems. This thesis presents experiments to better understand how conditional GANs can be used in probabilistic regression. Different versions of GANs are extended to the conditional case and evaluated on synthetic and real datasets. It is shown that conditional GANs can learn to estimate a wide range of different distributions and be competitive with existing probabilistic regression models.
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Albertazzi, Riccardo. "A study on the application of generative adversarial networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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High performance and nearly perfect accuracy are the standards required by OCR algorithms for industrial applications. In the last years research on Deep Learning has proven that Convolutional Neural Networks (CNNs) are a very powerful and robust tool for image analysis and classification; when applied to OCR tasks, CNNs are able to perform much better than previously adopted techniques and reach easily 99% accuracy. However, Deep Learning models' effectiveness relies on the quality of the data used to train them; this can become a problem since OCR tools can run for months without interruption, and during this period unpredictable variations (printer errors, background modifications, light conditions) could affect the accuracy of the trained system. We cannot expect that the final user who trains the tool will take thousands of training pictures under different conditions until all imaginable variations have been captured; we then have to be able to generate these variations programmatically. Generative Adversarial Networks (GANs) are a recent breakthrough in machine learning; these networks are able to learn the distribution of the input data and therefore generate realistic samples belonging to that distribution. This thesis' objective is learning how GANs work in detail and perform experiments on generative models that allow to create unseen variations of OCR training characters, thus allowing the whole OCR system to be more robust to future character variations.
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Book chapters on the topic "CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)"

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Kumar, Jatin, Indra Deep Mastan, and Shanmuganathan Raman. "FMD-cGAN: Fast Motion Deblurring Using Conditional Generative Adversarial Networks." In Communications in Computer and Information Science, 362–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11349-9_32.

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Conference papers on the topic "CONDITIONAL GENERATIVE ADVERARIAL NETWORKS (CGAN)"

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Pang, Yutian, and Yongming Liu. "Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects." In AIAA Scitech 2020 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-1853.

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Yang, Lu. "Conditional Generative Adversarial Networks (CGAN) for Abnormal Vibration of Aero Engine Analysis." In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2020. http://dx.doi.org/10.1109/iccasit50869.2020.9368622.

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Qi, Y., L. Su, J. Gu, and K. Li. "CE-CGAN: classification enhanced conditional generative adversarial networks for bearing fault diagnosis." In 12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.3125.

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Salimzadeh, S., D. Kasperczyk, and T. Kadeethum. "Predicting Ground Surface Deformation Induced by Pressurized Fractures Using Conditional Generative Adversarial Networks." In 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0218.

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ABSTRACT Hydraulic fracturing has been widely applied in subsurface to facilitate hydrocarbon flow or as a preconditioning method to promote normal caving behavior in mining operations. The success of the method prominently relies on the geometry of the induced fractures in the host rock. An array of high-resolution tiltmeters is commonly used to measure the displacement gradient (tilt angle) of ground surface during hydraulic fracturing operation, and to infer the geometry of the induced fractures. In this work, we present a machine-learned model that is capable of predicting the surface tilt resulting from a pressurized fracture, using Conditional Generative Adversarial Networks (cGAN). Fracture apertures along horizontal and vertical planes are given as input while surface tilt vectors are provided as output for the forward model. A 3D finite element model with discrete fractures is utilized as the Full Order Model (FOM) to create training data. The fractures are pressurized uniformly to induce a displacement at the ground surface. Images of the fracture aperture in XY and XZ planes, and surface tilt vector components (tilt X and tilt Y) are used for the training process. Three different loss functions are used, and the predicted tilts are compared to the real (FOM) tilts. The results show that the trained cGAN with Wasserstein loss and gradient penalty (W-model) can predict the ground surface tilt due to the pressurized fractures at variable dip angles. INTRODUCTION Hydraulic fracturing has been frequently applied as a preconditioning method to promote normal caving behavior in mining operations. The success of the method prominently relies on the geometry of the induced fractures in the stiff overburden layer. An array of high-resolution tiltmeters is commonly used to measure the displacement gradient (tilt angle) of ground surface during hydraulic fracturing operation, and to infer the geometry of the induced fractures or pressure plumes using simplified models (Lecampion et al., 2005; Salimzadeh et al., 2022).
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Nie, Zhenguo, Tong Lin, Haoliang Jiang, and Levent Burak Kara. "TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22675.

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Abstract In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results. We propose a new data-driven topology optimization model called TopologyGAN that takes advantage of various physical fields computed on the original, unoptimized material domain, as inputs to the generator of a conditional generative adversarial network (cGAN). Compared to a baseline cGAN, TopologyGAN achieves a nearly 3× reduction in the mean squared error and a 2.5× reduction in the mean absolute error on test problems involving previously unseen boundary conditions. Built on several existing network models, we also introduce a hybrid network called U-SE(Squeeze-and-Excitation)-ResNet for the generator that further increases the overall accuracy. We publicly share our full implementation and trained network.
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Toutouh, J., S. Nesmachnow, and D. G. Rossit. "Generative adversarial networks to model air pollution under uncertainty." In 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.20.

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Urbanization trends worldwide show a clear preference for motorized road mobility, which has led to a degradation of air quality in recent years. Modelling and forecasting ambient air pollution is a relevant problem because it helps decision-makers and urban city planners understand this phenomenon, which is a significant threat to citizens’ health. Generally, datadriven models suffer from a lack of data. This article addresses the issue of having limited access to road traffic density and pollution concentration data by applying deep generative models, specifically, Conditional Generative Adversarial Networks (CGAN). The main idea is to train CGANs to generate synthetic nitrogen dioxide concentration values given the road traffic density. The experimental data analysis from Montevideo (Uruguay) shows that the proposed method generates realistic (accurate and diverse) pollution data while using reduced computational resources.
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Chen, Hongrui, and Xingchen Liu. "Geometry Enhanced Generative Adversarial Networks for Random Heterogeneous Material Representation." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71918.

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Abstract The representation of material structure geometry is essential to the reconstruction, physical simulation, and the multiscale structure design with Random Heterogeneous Material (RHM). Traditional approaches to material structure representation often need to balance the trade-off between efficacy and accuracy. Recently, deep learning-based techniques have been adopted to reduce the computational time of RHM reconstruction. However, existing approaches generally lack guarantees over key RHM characteristics, including Minkowski functionals and correlation functions. We propose a novel approach to geometrically enhancing the deep learning-based RHM representation by introducing Minkowski functionals, a set of topological and geometrical characteristics of material structure, into the training of conditional Generative Adversarial Networks (cGAN). This hybrid approach combines the feature learning capability of deep learning with the well-established material structure characteristics, greatly improving the accuracy of the RHM representation while maintaining its efficiency. The effectiveness of the proposed hybrid approach is validated through the reconstruction of a wide range of natural and manmade materials, including Voronoi foam structures, femur, and sandstone. Through computational experiments, we demonstrate that geometrically enhancing the training of cGAN for RHM representation not only significantly decreases the representation error in Minkowski functionals between input sample materials and reconstructed results, but also improves the performance of other material structure characteristics, such as two-point correlation functions.
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Jiang, Haoliang, Zhenguo Nie, Roselyn Yeo, Amir Barati Farimani, and Levent Burak Kara. "StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22682.

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Abstract Using deep learning to analyze mechanical stress distributions has been gaining interest with the demand for fast stress analysis methods. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physics without prior knowledge of underlying equations. However, most studies restrict the variation of geometry or boundary conditions, making these methods difficult to be generalized to unseen configurations. We propose a conditional generative adversarial network (cGAN) model for predicting 2D von Mises stress distributions in solid structures. The cGAN learns to generate stress distributions conditioned by geometries, load, and boundary conditions through a two-player minimax game between two neural networks with no prior knowledge. By evaluating the generative network on two stress distribution datasets under multiple metrics, we demonstrate that our model can predict more accurate high-resolution stress distributions than a baseline convolutional neural network model, given various and complex cases of geometry, load and boundary conditions.
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Ziviani, Hugo Eduardo, Guillermo Cámara Chávez, and Mateus Coelho Silva. "Applying a Conditional GAN for Bone Suppression in Chest Radiography Images." In Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/semish.2022.222540.

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Bone suppression in radiography is a suitable technique to evaluate the health of soft tissues in exams. For instance, these techniques are essential in evaluating chest radiography images during the COVID-19 outbreak. The purpose of this work is to propose an alternative to solve the bone suppression task in chest radiography images using Generative Adversarial Networks (GANs). Specifically, we used a conditional GAN type (cGAN) to provide a bone-suppressed version of the initial image. To quantify the results, it was necessary to review the main metrics and some state-of-the-art papers related to ours. We compared our result to works from the literature that used the same dataset as the proposal or related techniques. The most used dataset was the Japanese Society of Radiological Technology (JSRT) in these works. With this set of images, we reached a PSNR index of 34.96, which was better than that reviewed in the literature, and a similarity coefficient, known as SSIM, of 0.94. As for the loss calculated by MS-SSIM, we obtained the lowest compared to the reviewed works.
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Sun, Xiaopeng, Muxingzi Li, Tianyu He, and Lubin Fan. "Enhance Image as You Like with Unpaired Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/140.

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Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.
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