Relevant bibliographies by topics / AdaDepth

Academic literature on the topic 'AdaDepth'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "AdaDepth"

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Gulland, A. "Ameyo Adadevoh." BMJ 349, dec16 14 (December 16, 2014): g7558. http://dx.doi.org/10.1136/bmj.g7558.

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Mandasari, Sartika, Desi Irfan, Wanayumini Wanayumini, and Rika Rosnelly. "COMPARISON OF SGD, ADADELTA, ADAM OPTIMIZATION IN GENDER CLASSIFICATION USING CNN." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, no. 3 (June 7, 2023): 345–54. http://dx.doi.org/10.33330/jurteksi.v9i3.2067.

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Abstract: Gender classification is one of the most important tasks of video analysis. A machine learning-based approach was presented to identify male and female facial images with a data set of 2000 images taken from kaggles. This method plays a role in finding the weight value that gives the best output value. This study uses the most appropriate learning rate of each optimization method as a criterion for stopping training. The results showed that the Artificial Neural Network with Adam optimization produced the highest accuracy, which was 91.5% compared to the SGD and ADADELTA optimization methods. Deep Learning techniques that are applied extensively to image recognition used utilize Adam's optimizer method. Keywords: artificial neural networks; adadelta; adam; gender; sgm; Abstrak: Klasifikasi gender adalah salah satu tugas analisis video yang paling penting. Pendekatan berbasis machine learning disajikan untuk mengidentifikasi gambar wajah Pria dan Wanita dengan kumpulan data sebanyak 2000 gambar yang diambil dari kaggle. Metode ini berperan dalam menemukan nilai bobot yang memberikan nilai keluaran terbaik. Penelitian ini menggunakan learning rate yang paling sesuai dari masing-masing metode optimasi sebagai kriteria pemberhentian pelatihan. Hasil penelitian menunjukkan Jaringan Saraf Tiruan dengan optimasi Adam menghasilkan akurasi tertinggi yaitu 91,5 % dibandingkan dengan dengan metode optimasi SGD dan ADADELTA. Teknik Deep Learning yang diterapkan secara ekstensif pada pengenalan gambar yang digunakan memanfaatkan metode optimizer Adam. Kata kunci: Adadelta; Adam; Jaringan Syaraf; Gender; Tiruan; SGM;
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Shete, Saurabh, and Avinash Marbhal. "Experiments on Gaussian Dropout and Adadelta for Hepatocellular Carcinoma." International Journal for Research in Applied Science and Engineering Technology, no. 12 (December 31, 2022): 396–403. http://dx.doi.org/10.22214/ijraset.2022.47883.

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Abstract: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Hepatocellular carcinoma occurs most often in people with chronic liver diseases, such as cirrhosis caused by hepatitis B or hepatitis C infection. Early detection and accurate predictive analysis play a pivotal role in the totality of the human population and are of extreme importance for enhanced life expectancy. With the advent of computation, there are well-defined publicly available datasets that can be leveraged for an accurate and temporarily efficient understanding of HCC. There exists preliminary work on these data samples that leverage classical machine learning algorithms, however, the state of the art is heavily skewed towards the deep neural networks. To improve the existing approaches, this paper seeks to leverage Gaussian Dropout, a variant of the standard dropout, for its remedial action on overfitting and related qualities. The pipeline is also tested and experimented with Adadelta, to obtain the applicability of these additions to a standard feed-forward network. These experiments and the methodologies considered for appendage to the baseline network are thoroughly assessed and validated by using the accepted metrics on an iteratively imputed dataset on multiple train-test data distributions.
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Сіряк, Р. В., І. С. Скарга-Бандурова, and T. O. Білобородова. "Towards an empirical hyperparameters optimization in CNN." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 87–91. http://dx.doi.org/10.33216/1998-7927-2019-253-5-87-91.

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The necessity of creating a model of recognition of gestures based on convolutional neural network that effective not only in pattern recognition, but also in terms of learning speed and resource intensity, is substantiated. In this regard, the work solved the problem of optimization of hyperparameters and the selection of the best optimizer backpropagation errors. To implement the tasks, a model was created that can recognize hand gestures, both from a single image and from streaming video.When choosing an optimizer, two adaptive methods were tested - Adadelta and Adam. The experiments confirmed the high efficiency of Adadelta, however, when compared with Adam, it showed more than twice as long network training.
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Irfan, Desi, Rika Rosnelly, Masri Wahyuni, Jaka Tirta Samudra, and Aditia Rangga. "PERBANDINGAN OPTIMASI SGD, ADADELTA, DAN ADAM DALAM KLASIFIKASI HYDRANGEA MENGGUNAKAN CNN." JOURNAL OF SCIENCE AND SOCIAL RESEARCH 5, no. 2 (June 27, 2022): 244. http://dx.doi.org/10.54314/jssr.v5i2.789.

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Abstract - invasive species are threatening indigenous species habitat in many countries around the world. Nowadays, the monitoring method relies on scientists. Scientists are skilled to see the determined areas and record the living species. Applying high skill labors requires high cost, inefficient time and limited scope as the large area cannot be reached by the man. In this research, engine based learning approach was presented to identify the image of invasive hydrangea (indigenous species from Asia) with data collection around 800 images taken form the Brazil national forest and Hydrangea appears in some images. Gradient Descent optimization method is frequently used for artificial neural network. This method roles to discover standard grade for the best output. The Gradient Descent method role play is minimizing the cost function grade by changing the parameter grade step by step. Three optimization methods have been implemented namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam in the artificial neural network (Ann) for classifying aritmia data [32]. This research used the most suitable error grade limitation from each optimization method as the indicators at the end of the training. The result of this research showed that artificial nerve tissue using Adam optimization gets the highest accuration compared with SDG and ADADELTA optimization methods. Deep Learning Technique applied extensively in image introduction is Adam optimization. The training model has reached accuration to 83, 5 % and showed properness of approach conducted. Keyword: SGD, Adadelta, Adam, Optimizer FunctionAbstrak— Spesies invasif mengancam habitat spesies asli di banyak negara di dunia. Saat ini dalam metode pemantauan mereka tergantung pada pengetahuan ahli. Ilmuwan terlatih mengunjungi area yang ditentukan dan mencatat spesies yang menghuninya. Menggunakan tenaga kerja berkualifikasi tinggi seperti itu membutuhkan biaya yang mahal, tidak efisien waktu dan jangkauan yang terbatas karena manusia tidak dapat mencakup area yang luas. Dalam makalah ini, pendekatan berbasis pembelajaran mesin disajikan untuk mengidentifikasi gambar hydrangea invasif (spesies invasif asli Asia) dengan kumpulan data yang berisi sekitar 800 gambar yang diambil di hutan nasional Brasil dan di beberapa gambar terdapat Hydrangea.  Metode optimasi Gradient Descent sering digunakan untuk pelatihan Jaringan Syaraf Tiruan (JST). Metode ini berperan dalam menemukan nilai bobot yang memberikan nilai keluaran terbaik. Prinsip kerja metode Gradient Descent adalah memperkecil nilai fungsi biaya dengan mengubah nilai parameter selangkah demi selangkah. Telah diimplementasikan tiga buah metode optimasi yaitu Stochastic Gradient Descent (SGD), ADADELTA, dan Adam pada sistem Jaringan Saraf Tiruan untuk klasifikasi data aritmia [32]. Penelitian ini menggunakan batas nilai kesalahan yang paling sesuai dari masing-masing metode optimasi  sebagai kriteria pemberhentian pelatihan. Hasil penelitian menunjukkan Jaringan Saraf Tiruan dengan optimasi Adam menghasilkan akurasi tertinggi dibandingkan dengan dengan metode optimasi SGD dan ADADELTA.Teknik Deep Learning  yang diterapkan secara ekstensif pada pengenalan gambar yang digunakan memanfaatkan metode optimizer Adam  . Model yang kita latih menggunakan fungsi optimisasi Adam mencapai akurasi 83,5% pada tes yang lakukan, menunjukkan kelayakan pada  pendekatan yang dilakukan .Kata Kunci— SGD, Adadelta, Adam, Fungsi Optimasi
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Assodiky, Hilmy, Iwan Syarif, and Tessy Badriyah. "Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate." EMITTER International Journal of Engineering Technology 6, no. 1 (July 10, 2018): 75–91. http://dx.doi.org/10.24003/emitter.v6i1.265.

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Arrhythmia is a heartbeat abnormality that can be harmless or harmful. It depends on what kind of arrhythmia that the patient suffers. People with arrhythmia usually feel the same physical symptoms but every arrhythmia requires different treatments. For arrhythmia detection, the cardiologist uses electrocardiogram that represents the cardiac electrical activity. And it is a kind of sequential data with high complexity. So the high performance classification method to help the arrhythmia detection is needed. In this paper, Long Short-Term Memory (LSTM) method was used to classify the arrhythmia. The performance was boosted by using AdaDelta as the adaptive learning rate method. As a comparison, it was compared to LSTM without adaptive learning rate. And the best result that showed high accuracy was obtained by using LSTM with AdaDelta. The correct classification rate was 98% for train data and 97% for test data.
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Shedriko, Shedriko, and Muhammad Firdaus. "Perbandingan Optimizer Adagrad, Adadelta dan Adam dalam Klasifikasi Gambar Menggunakan Deep Learning." STRING (Satuan Tulisan Riset dan Inovasi Teknologi) 8, no. 1 (August 5, 2023): 103. http://dx.doi.org/10.30998/string.v8i1.16564.

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Azar, Ahmad Taher, Mohamed Tounsi, Suliman Mohamed Fati, Yasir Javed, Syed Umar Amin, Zafar Iqbal Khan, Shrooq Alsenan, and Jothi Ganesan. "Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques." International Journal of Sociotechnology and Knowledge Development 15, no. 1 (July 24, 2023): 1–28. http://dx.doi.org/10.4018/ijskd.326629.

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Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.
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Chen, Jiayu, Jinsheng Liu, Long Han, Mingru Ci, Dongbo Che, Lihong Guo, and Hongjun Yu. "Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems." Applied Sciences 12, no. 6 (March 16, 2022): 3009. http://dx.doi.org/10.3390/app12063009.

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Stochastic parallel gradient descent (SPGD) algorithm is one of the most promising methods for effective coherent beam combination. However, the algorithm also has some disadvantages, such as slow convergence speed and local extremum. This paper proposes an AdaDelSPGD algorithm, which combines an AdaDelta algorithm with a SPGD algorithm, and improves the traditional AdaDelta algorithm with adaptive gain coefficient. It is worth noting that the adaptive gain coefficient can be adjusted in real time to improve the convergence rate. The effectiveness of the proposed algorithm is verified by relevant simulation experiments, and the results show that the proposed algorithm can significantly improve the convergence speed. Following the experiments with the fiber laser-phased array multiplex communication system, we can draw the conclusion that the addition of communication modulation reduces the beam quality, and the higher the modulation frequency, the worse the beam quality. However, adding the SPGD algorithm can improve the beam quality. The AdaDelSPGD algorithm proposed in this paper can further improve the beam quality, and the bit error rate of communication is also decreased after testing. This provides a foundation for further research on the fiber laser-phased array multiplex communication system.
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Dogo, Eustace M., Oluwatobi J. Afolabi, and Bhekisipho Twala. "On the Relative Impact of Optimizers on Convolutional Neural Networks with Varying Depth and Width for Image Classification." Applied Sciences 12, no. 23 (November 23, 2022): 11976. http://dx.doi.org/10.3390/app122311976.

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The continued increase in computing resources is one key factor that is allowing deep learning researchers to scale, design and train new and complex convolutional neural network (CNN) architectures in terms of varying width, depth, or both width and depth to improve performance for a variety of problems. The contributions of this study include an uncovering of how different optimization algorithms impact CNN architectural setups with variations in width, depth, and both width/depth. Specifically in this study, three different CNN architectural setups in combination with nine different optimization algorithms—namely SGD vanilla, with momentum, and with Nesterov momentum, RMSProp, ADAM, ADAGrad, ADADelta, ADAMax, and NADAM—are trained and evaluated using three publicly available benchmark image classification datasets. Through extensive experimentation, we analyze the output predictions of the different optimizers with the CNN architectures using accuracy, convergence speed, and loss function as performance metrics. Findings based on the overall results obtained across the three image classification datasets show that ADAM and NADAM achieved superior performances with wider and deeper/wider setups, respectively, while ADADelta was the worst performer, especially with the deeper CNN architectural setup.
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Dissertations / Theses on the topic "AdaDepth"

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Kundu, Jogendra Nath. "Self-Supervised Domain Adaptation Frameworks for Computer Vision Tasks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5782.

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There is a strong incentive to build intelligent machines that can understand and adapt to changes in the visual world without human supervision. While humans and animals learn to perceive the world on their own, almost all state-of-the-art vision systems heavily rely on external supervision from millions of manually annotated training examples. Gathering such large-scale manual annotations for structured vision tasks, such as monocular depth estimation, scene segmentation, human pose estimation, faces several practical limitations. Usually, the annotations are gathered in two broad ways; 1) via specialized instruments (sensors) or laboratory setups, 2) via manual annotations. Both processes have several drawbacks. While human annotations are expensive, scarce, or error-prone; instrument-based annotations are often noisy or limited to specific laboratory environments. Such limitations not only stand as a major bottleneck in our efforts to gather unambiguous ground-truth but also limit the diversity in the collected labeled dataset. This motivates us to develop innovative ways to utilize synthetic environments to create labeled synthetic datasets with noise-free unambiguous ground-truths. However, the performance of models trained on such synthetic data markedly degrades when tested on real-world samples due to input distribution shift (a.k.a. domain shift). Unsupervised domain adaptation (DA) seeks learning techniques that can minimize the domain discrepancy between a labeled source and an unlabeled target. However, it mostly remains unexplored for challenging structured prediction based vision tasks. Motivated by the above observations, my research focuses on addressing the following key aspects: (1) Developing algorithms that support improved transferability to domain and task shifts, (2) Leveraging inter-entity or cross-modal relationships to develop self-supervised objectives, and (3) Instilling natural priors to constrain the model output within the realm of natural distributions. First, we present AdaDepth - an unsupervised domain adaptation (DA) strategy for the pixel-wise regression task of monocular depth estimation. Mode collapse is a common phenomenon observed during adversarial training in the absence of paired supervision. Without access to target depth-maps, we address this challenge using a novel content congruent regularization technique. In a follow-up work, we introduced UM-Adapt, a unified framework to address two distinct objectives in a multi-task adaptation framework, i.e., a) achieving balanced performance across all tasks and b) performing domain adaptation in an unsupervised setting. This is realized using two novel regularization strategies; Contour-based content regularization and exploitation of inter-task coherency using a novel cross-task distillation module. Moving forward, we identified certain key issues in existing domain adaptation algorithms that hinder their practical deployability to a large extent. Existing approaches demand the coexistence of source and target data, which is highly impractical in scenarios where data-sharing is restricted due to proprietary or privacy concerns. To address this, we propose a new setting termed as Source-Free DA and tailored learning protocols for the dense prediction task of semantic segmentation and image classification in both with and without category shift scenarios. Further, we investigate the problem of Self-supervised Domain Adaptation for the challenging monocular 3D human pose estimation task. The key differentiating factor in our approach is the idea of infusing model-based structural prior as a means to constrain the pose estimation predictions within the realm of natural pose and shape distributions. Towards self-supervised learning, our contribution lies in the effective use of new inter-entity relationships to discern the co-salient foreground appearance and thereby the corresponding pose from just a pair of images having diverse backgrounds. Unlike self-supervised solutions that aim for better generalization, self-adaptive solutions aim for target-specific adaptation, i.e., adaptation to deployment-specific environmental attributes. To this end, we propose a self-adaptive method to align the latent space of human pose from unpaired image-to-latent and the pose-to-latent, by enforcing well-formed non-local latent space rules available for unpaired image (or video) and pose (or motion) domains. This idea of non-local relation distillation against the broadly employed general contrastive learning techniques shows significant improvements in the self-adaptation performance. Further, in a recent work, we propose a novel way to effectively utilize uncertainty estimation for out-of-distribution (OOD) detection, and thus enabling inference-time self-adaptation. The ability to discern OOD samples allows a model to assess when to perform re-adaptation while deployed in a continually changing environment. Such solutions are in high demand for enabling effective real-world deployment across various industries, from virtual and augmented reality to gaming and health-care applications.
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Book chapters on the topic "AdaDepth"

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Mujhid, Almuzhidul, Sugiyarto Surono, Nursyiva Irsalinda, and Aris Thobirin. "Comparison and Combination of Leaky ReLU and ReLU Activation Function and Three Optimizers on Deep CNN for COVID-19 Detection." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220369.

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COVID-19 detection is an interesting field of study in the medical world and the commonly used method is classification. In determining the best detection model, several classification architectures, such as SVM, KNN, and CNN were utilized. The CNN is a changeable architecture due to having combinations of varying numbers of hidden layers or different activation and optimizer functions. Therefore, this study uses a deep CNN architecture with a combination of Leaky ReLU activation functions and 3 different optimizers, which include Adagrad, Adadelta, and Adamax. The results showed that the combination of the Leaky ReLU activation function and the Adamax optimizer produced good and stable accuracy in the CRX and CT datasets.
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Conference papers on the topic "AdaDepth"

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Kundu, Jogendra Nath, Phani Krishna Uppala, Anuj Pahuja, and R. Venkatesh Babu. "AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00281.

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P, Mathivanan, Kokilambal S, Snehashri V, and Swetha A. "Intelligent Content Based Image Retrieval Model Using Adadelta Optimized Residual Network." In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2021. http://dx.doi.org/10.1109/icscan53069.2021.9526470.

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Gill, Kanwarpartap Singh, Avinash Sharma, Vatsala Anand, and Rupesh Gupta. "Brain Tumor Detection using VGG19 model on Adadelta and SGD Optimizer." In 2022 6th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2022. http://dx.doi.org/10.1109/iceca55336.2022.10009496.

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Santos, Stefane A., Andressa G. Moreira, and Ialis C. P. Junior. "Análise comparativa da influência de otimizadores no desempenho de uma CNN para detecção do câncer de mama." In Escola Regional de Computação Ceará, Maranhão, Piauí. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercemapi.2021.17901.

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O campo da inteligência artificial (IA) apresenta notáveis avanços na medicina. Estudos analisam a aplicação de Redes Neurais Convolucionais para a detecção de câncer de mama. Neste artigo, é realizada uma análise comparativa entre os métodos de otimização (Adam, Adadelta, Adagrad, Adamax, Nadam, RMSprop) aplicados a uma arquitetura VggNet16 para a classificação de neoplasias em imagens histopatológicas. Os experimentos foram realizados com a criação de modelos para os fatores de ampliação (40x, 100x, 200x, 400x) das imagens extraídas do dataset BreakHis. O otimizador Adam obteve o melhor resultado para o conjunto de imagens, especificamente na base 400x.
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Singh, Rahul, Avinash Sharma, Neha Sharma, and Rupesh Gupta. "Impact of Adam, Adadelta, SGD on CNN for White Blood Cell Classification." In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2023. http://dx.doi.org/10.1109/icssit55814.2023.10061068.

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Devi, M. Shyamala, R. Aruna, D. Raja Rajeswari, and R. Sai Manogna. "Conv2D Xception Adadelta Gradient Descent Learning Rate Deep learning Optimizer for Plant Species Classification." In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2023. http://dx.doi.org/10.1109/icaect57570.2023.10117710.

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Zhao, Yangyang, Chaoliang Wang, Lei Guo, Tao Xiao, and Baiqiang Shen. "A Suppression Strategy of Bus Voltage Transient Sag Caused by Heat Pump with AdaDelta Algorithm." In 2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST). IEEE, 2022. http://dx.doi.org/10.1109/iaecst57965.2022.10061981.

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