Self-Supervised GANs via Auxiliary Rotation Loss

Generative Adversarial Nets

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GAN（Generative Adversarial Nets）

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Unsupervised representation learning with deep convolutional generative adversarial networks

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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

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Overcoming catastrophic forgetting in neural networks

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Improved Techniques for Training GANs

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Improved training of Wasserstein GANs

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Context Encoders: Feature Learning by Inpainting

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“Least squares generative adversarial networks,”

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Progressive Growing of GANs for Improved Quality, Stability, and Variation

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Colorful Image Colorization

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Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem

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Large Scale GAN Training for High Fidelity Natural Image Synthesis

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Spectral Normalization for Generative Adversarial Networks

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Self-Attention Generative Adversarial Networks

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Conditional Image Synthesis With Auxiliary Classifier GANsPreprint

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Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

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Deep Clustering for Unsupervised Learning of Visual Features

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Unsupervised Visual Representation Learning by Context Prediction

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Unsupervised Representation Learning by Predicting Image Rotations

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Catastrophic forgetting in connectionist networks

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Adversarial Feature LearningPreprint

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Learning from Simulated and Unsupervised Images through Adversarial Training

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Continual Learning Through Synaptic Intelligence

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LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the LoopPreprint

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Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours

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Which Training Methods for GANs do actually Converge?Preprint

Mescheder, L., Geiger, A., & Nowozin, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04406 · Cited by 1,029 · see citing works

Assessing Generative Models via Precision and RecallPreprint

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Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksPreprint

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Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

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Revisiting Self-Supervised Visual Representation Learning

Kolesnikov, A., Zhai, X., & Beyer, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00202 · Cited by 675 · see citing works

Learning to See by Moving

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Unsupervised Representation Learning by Sorting Sequences

Lee, H.-Y., Huang, J., Singh, M., & Yang, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.79 · Cited by 529 · see citing works

cGANs with Projection DiscriminatorPreprint

Miyato, T., & Koyama, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.05637 · Cited by 464 · see citing works

Modulating early visual processing by languagePreprint

de Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., & Courville, A. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00683 · Cited by 368 · see citing works

A Note on the Inception ScorePreprint

Barratt, S. T., & Sharma, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01973 · Cited by 366 · see citing works

GANs Trained by a Two Time-Scale Update Rule Converge to a Nash EquilibriumPreprint

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., & Hochreiter, S. (2017) · arXiv (Cornell University) · Cited by 364 · see citing works

cGANs with Projection Discriminator

Miyato, T., & Koyama, M. (2018) · Cited by 293 · see citing works

The GAN Landscape: Losses, Architectures, Regularization, and NormalizationPreprint

Kurach, K., Lučić, M., Zhai, X., Michalski, M., & Gelly, S. (2018) · arXiv (Cornell University) · Cited by 125 · see citing works

Improvements to Context Based Self-Supervised Learning

Mundhenk, T. N., Ho, D., & Chen, B. Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00973 · Cited by 122 · see citing works

Continual Learning in Generative Adversarial NetsPreprint

Seff, A., Beatson, A., Suo, D., & Liu, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08395 · Cited by 88 · see citing works

On Self Modulation for Generative Adversarial NetworksPreprint

Chen, T., Lučić, M., Houlsby, N., & Gelly, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.01365 · Cited by 82 · see citing works

Grasp2Vec: Learning Object Representations from Self-Supervised GraspingPreprint

Jang, E., Devin, C., Vanhoucke, V., & Levine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.06964 · Cited by 82 · see citing works

On Catastrophic Forgetting and Mode Collapse in Generative Adversarial NetworksPreprint

Thanh-Tung, H., & Tran, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.04015 · Cited by 41 · see citing works

Generative Adversarial Network Training is a Continual Learning ProblemPreprint

Liang, K. J., Li, C., Wang, G., & Carin, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.11083 · Cited by 36 · see citing works

An Online Learning Approach to Generative Adversarial NetworksPreprint

Grnarova, P., Levy, K. Y., Lucchi, A., Hofmann, T., & Krause, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.03269 · Cited by 29 · see citing works

Memorization Precedes Generation: Learning Unsupervised GANs with Memory NetworksPreprint

Kim, Y., Kim, M., & Kim, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01500 · Cited by 26 · see citing works

Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient AnalysisPreprint

Zhou, Z., Song, Y., Yu, L., & Yu, Y. H. (2018) · arXiv (Cornell University) · Cited by 3 · see citing works

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · see citing works

Big Self-Supervised Models are Strong Semi-Supervised LearnersPreprint

Chen, T., Kornblith, S., Swersky, K., Norouzi, M., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10029 · Cited by 1,480 · see citing works

Untitled work

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Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3116668 · Cited by 735 · see citing works

A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends

Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., & Tao, D. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3415112 · Cited by 622 · see citing works

A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT

Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02443-6 · Cited by 704 · see citing works

GAN-based anomaly detection: A review

Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., & Ding, N. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.093 · Cited by 404 · see citing works

Large Scale Adversarial Representation LearningPreprint

Donahue, J., & Simonyan, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02544 · Cited by 398 · see citing works

Differentiable Augmentation for Data-Efficient GAN TrainingPreprint

Zhao, S., Liu, Z., Lin, J., Zhu, J.-Y., & Han, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10738 · Cited by 417 · see citing works

On Data Augmentation for GAN Training

Tran, N.-T., Tran, V.-H., Nguyen, N.-B., Nguyen, T.-K., & Cheung, N. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049346 · Cited by 399 · see citing works

A U-Net Based Discriminator for Generative Adversarial Networks

Schönfeld, E., Schiele, B., & Khoreva, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00823 · Cited by 353 · see citing works

Models Genesis

Zhou, Z., Sodha, V., Pang, J., Gotway, M. B., & Liang, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101840 · Cited by 353 · see citing works

TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale UpPreprint

Jiang, Y., Chang, S., & Wang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07074 · Cited by 373 · see citing works

Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods

Qi, G.-J., & Luo, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3031898 · Cited by 315 · see citing works

Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection

Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01815 · Cited by 307 · see citing works

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267 · see citing works

Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyPreprint

Wang, Z., She, Q., & Ward, T. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01529 · Cited by 254 · see citing works

Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

Chen, Y., Mancini, M., Zhu, X., & Akata, Z. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3201576 · Cited by 235 · see citing works

DU-GAN: Generative Adversarial Networks With Dual-Domain U-Net-Based Discriminators for Low-Dose CT Denoising

Huang, Z., Zhang, J., Zhang, Y., & Shan, H. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3128703 · Cited by 235 · see citing works

Co 2 L: Contrastive Continual Learning

Cha, H., Lee, J., & Shin, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00938 · Cited by 225 · see citing works

Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

Wang, S., Yu, L., Li, C., Fu, C., & Heng, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_10 · Cited by 206 · see citing works

Consistency Regularization for Generative Adversarial NetworksPreprint

Zhang, H., Zhang, Z., Odena, A., & Lee, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12027 · Cited by 218 · see citing works

Review on self-supervised image recognition using deep neural networks

Ohri, K., & Kumar, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107090 · Cited by 179 · see citing works

Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image SynthesisPreprint

Liu, B., Zhu, Y., Song, K., & Elgammal, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04775 · Cited by 176 · see citing works

Self-Supervised Attentive Generative Adversarial Networks for Video Anomaly Detection

Huang, C., Wen, J., Xu, Y. D., Jiang, Q., Yang, J. Y., Wang, Y., & Zhang, D. D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3159538 · Cited by 163 · see citing works

A Large-scale Study of Representation Learning with the Visual Task Adaptation BenchmarkPreprint

Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lučić, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04867 · Cited by 249 · see citing works

Toward Self-Learning Edge Intelligence in 6G

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A Survey on Contrastive Self-Supervised Learning

Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., & Makedon, F. (2020) · Technologies · doi:10.3390/technologies9010002 · Cited by 154 · see citing works

TransGAN: Two Transformers Can Make One Strong GANPreprint

Jiang, Y., Chang, S., & Wang, Z. (2021) · arXiv (Cornell University) · Cited by 147 · see citing works

Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANsPreprint

Mo, S., Cho, M., & Shin, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10964 · Cited by 167 · see citing works

High-Fidelity Image Generation With Fewer LabelsPreprint

Lučić, M., Tschannen, M., Ritter, M., Zhai, X., Bachem, O., & Gelly, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02271 · Cited by 126 · see citing works

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation

Hu, X., Zhou, X., Huang, Q., Shi, Z., Li, S., & Li, Q. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01775 · Cited by 117 · see citing works

Image Augmentations for GAN TrainingPreprint

Zhao, Z., Zhang, Z., Chen, T., Singh, S., & Zhang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02595 · Cited by 125 · see citing works

Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works

Tao, C., Qi, J., Guo, M., Zhu, Q., & Li, H. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3276853 · Cited by 109 · see citing works

From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape

McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works

A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

Morales, E. F., Murrieta-Cid, R., Becerra, I., & Esquivel-Basaldua, M. A. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00398-z · Cited by 101 · see citing works

On Robustness and Transferability of Convolutional Neural Networks

Djolonga, J., Yung, J., Tschannen, M., Romijnders, R., Beyer, L., Kolesnikov, A., Puigcerver, J., Minderer, M., D'Amour, A., Moldovan, D., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01619 · Cited by 98 · see citing works

How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

Lin, H., Liu, Y., Li, S., & Qu, X. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123744 · Cited by 99 · see citing works

GAN based augmentation using a hybrid loss function for dermoscopy images

Göçeri, E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10897-x · Cited by 94 · see citing works

Generative Hierarchical Features from Synthesizing Images

Xu, Y., Shen, Y., Zhu, J., Yang, C., & Zhou, B. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00441 · Cited by 87 · see citing works

Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations

Chowdhury, A., Rosenthal, J., Waring, J., & Umeton, R. (2021) · Informatics · doi:10.3390/informatics8030059 · Cited by 85 · see citing works

Zero-Shot Stance Detection via Contrastive Learning

Liang, B., Chen, Z.-X., Gui, L., He, Y.-L., Yang, M., & Xu, R. (2022) · The Web Conference · doi:10.1145/3485447.3511994 · Cited by 102 · see citing works

Self-Supervised Deep Visual Odometry With Online Adaptation

Li, S., Wang, X., Cao, Y., Xue, F., Yan, Z., & Zha, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00637 · Cited by 78 · see citing works

Brain stroke lesion segmentation using consistent perception generative adversarial network

Wang, S., Chen, Z., You, S., Wang, B.-C., Shen, Y., & Lei, B. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06816-8 · Cited by 77 · see citing works

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

Gidaris, S., Bursuc, A., Puy, G., Nikos Komodakis, A., Cord, M., & Pérez, P. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00676 · Cited by 86 · see citing works

Self-Supervised Learning for Scene Classification in Remote Sensing: Current State of the Art and Perspectives

Berg, P., Pham, M., & Courty, N. (2022) · Remote Sensing · doi:10.3390/rs14163995 · Cited by 77 · see citing works

When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework

Zhao, Z., Luo, Z., Li, J., Chen, C., & Piao, Y. (2020) · Remote Sensing · doi:10.3390/rs12203276 · Cited by 76 · see citing works

A Systematic Survey of Regularization and Normalization in GANs

Li, Z., Usman, M., Tao, R., Xia, P., Wang, C., Chen, H., & Li, B. (2022) · ACM Computing Surveys · doi:10.1145/3569928 · Cited by 74 · see citing works

A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

Koga, Y., Miyazaki, H., & Shibasaki, R. (2020) · Remote Sensing · doi:10.3390/rs12030575 · Cited by 73 · see citing works

Dual Contradistinctive Generative Autoencoder

Parmar, G., Li, D., Lee, K., & Tu, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00088 · Cited by 70 · see citing works

A generative adversarial network for synthetization of regions of interest based on digital mammograms

Oyelade, O. N., Ezugwu, A. E.-S., Almutairi, M. S., Saha, A. K., Abualigah, L., & Chiroma, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-09929-9 · Cited by 71 · see citing works

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation LearningPreprint

Lee, K., Zhu, Y., Sohn, K., Li, C., Shin, J., & Lee, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08887 · Cited by 72 · see citing works

InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning

Lee, K. S., Tran, N.-T., & Cheung, N. M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00399 · Cited by 66 · see citing works

Mitigating Intensity Bias in Shadow Detection via Feature Decomposition and Reweighting

Zhu, L., Xu, K., Ke, Z., & Lau, R. W. H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00466 · Cited by 65 · see citing works

Deepfake Network Architecture Attribution

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Improved Transformer for High-Resolution GANsPreprint

Zhao, L., Zhang, Z., Chen, T., Metaxas, D., & Zhang, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07631 · Cited by 64 · see citing works

Unsupervised Real-World Super Resolution with Cycle Generative Adversarial Network and Domain Discriminator

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Towards Discovery and Attribution of Open-world GAN Generated Images

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Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond

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Dual Contrastive Loss and Attention for GANs

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HDR Environment Map Estimation for Real-Time Augmented Reality

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SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving

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SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Chen, Y., Li, G., Jin, C., Liu, S., & Li, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16196 · Cited by 51 · see citing works

A New Perspective on Stabilizing GANs Training: Direct Adversarial Training

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A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN

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Conditional Self-Supervised Learning for Few-Shot Classification

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Continual Learning on Noisy Data Streams via Self-Purified Replay

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Quaternion Generative Adversarial Networks

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Rethinking Data Augmentation: Self-Supervision and Self-DistillationPreprint

Lee, H., Hwang, S. J., & Shin, J. (2019) · arXiv (Cornell University) · Cited by 43 · see citing works

Auxiliary Training: Towards Accurate and Robust Models

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Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsPreprint

Wu, M., Mosse, M., Zhuang, C., Yamins, D. L. K., & Goodman, N. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02037 · Cited by 52 · see citing works

GridMix: Strong regularization through local context mapping

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Mitigating Label Noise in GANs via Enhanced Spectral Normalization

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Training GANs with Stronger Augmentations via Contrastive DiscriminatorPreprint

Jeong, J., & Shin, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.09742 · Cited by 39 · see citing works

Data-Efficient Instance Generation from Instance DiscriminationPreprint

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An effective data enhancement method of deep learning for small weld data defect identification

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SIRST-5K: Exploring Massive Negatives Synthesis With Self-Supervised Learning for Robust Infrared Small Target Detection

Lu, Y., Lin, Y., Wu, H., Xian, X., Shi, Y., & Lin, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3387125 · Cited by 36 · see citing works

Improved Contrastive Divergence Training of Energy Based Models

Du, Y., Li, S., Tenenbaum, J., & Mordatch, I. (2020) · arXiv · doi:10.48550/arxiv.2012.01316 · Cited by 36 · see citing works

Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification

Guo, D., Xia, Y. J., & Luo, X. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3056883 · Cited by 36 · see citing works

Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images

Sock, J., Garcia-Hernando, G., Armagan, A., & Kim, T. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00039 · Cited by 34 · see citing works

A Style-aware Discriminator for Controllable Image Translation

Kim, K. H., Park, S., Jeon, E., Kim, T., & Kim, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01770 · Cited by 33 · see citing works

Decode-MOT: How Can We Hurdle Frames to Go Beyond Tracking-by-Detection?

Lee, S.-H., Park, D.-H., & Bae, S. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3298538 · Cited by 33 · see citing works

Data Enhancement for Plant Disease Classification Using Generated Lesions

Sun, R., Zhang, M., Yang, K., & Liu, J. (2020) · Applied Sciences · doi:10.3390/app10020466 · Cited by 34 · see citing works

Noise Robust Generative Adversarial Networks

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Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video

Cho, H., Kim, T., Chang, H. J., & Hwang, W. (2021) · IEEE Access · doi:10.1109/access.2021.3084840 · Cited by 52 · see citing works

Simple Yet Effective Way for Improving the Performance of GAN

Yeo, Y.-J., Shin, Y.-G., Park, S., & Ko, S.-J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3045000 · Cited by 28 · see citing works

DeltaGAN: Towards Diverse Few-Shot Image Generation with Sample-Specific Delta

Hong, Y., Niu, L., Zhang, J., & Zhang, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19787-1_15 · Cited by 28 · see citing works

Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., & Zheng, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_30 · Cited by 27 · see citing works

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information

Huang, J., Ding, W., Lv, J., Yang, J., Dong, H., Ser, J. D., Xia, J., Ren, T., Wong, S. T. C., & Yang, G. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03092-w · Cited by 27 · see citing works

Self-Supervised Dense Consistency Regularization for Image-to-Image Translation

Ko, M., Cha, E., Suh, S., Lee, H., Han, J., Shin, J., & Han, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01776 · Cited by 27 · see citing works

Closing the Gap Between Theory and Practice During Alternating Optimization for GANs

Chen, Y., Sun, S., Li, G., Gao, W., & Li, T. H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3274221 · Cited by 27 · see citing works

Noise aware content-noise complementary GAN with local and global discrimination for low-dose CT denoising

Sarkar, K., Bag, S., & Tripathi, P. C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127473 · Cited by 26 · see citing works

Self-supervised intermittent fault detection for analog circuits guided by prior knowledge

Fang, X., Qu, J., & Chai, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109108 · Cited by 26 · see citing works

Inducing Optimal Attribute Representations for Conditional GANs

Bhattarai, B., & Kim, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_5 · Cited by 25 · see citing works

Two-stage framework with improved U-Net based on self-supervised contrastive learning for pavement crack segmentation

Song, Q., Yao, W., Tian, H., Guo, Y., Muniyandi, R. C., & An, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122406 · Cited by 25 · see citing works

Can Semantic Labels Assist Self-Supervised Visual Representation Learning?

Wei, L., Xie, L., He, J., Zhang, X., & Tian, Q. C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20166 · Cited by 26 · see citing works

Image Enhanced Rotation Prediction for Self-Supervised Learning

Yamaguchi, S., Kanai, S., Shioda, T., & Takeda, S. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506132 · Cited by 25 · see citing works

Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket PerspectivePreprint

Chen, T., Cheng, Y., Gan, Z., Liu, J., & Wang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.00397 · Cited by 29 · see citing works

Self-Guided Diffusion Models

Hu, V. T., Zhang, D. W., Asano, Y. M., Burghouts, G., & Snoek, C. G. M. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.01766 · Cited by 56 · see citing works

Generative Adversarial Networks

Cohen, G., & Giryes, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_17 · Cited by 33 · see citing works

Self-Supervised Generative Adversarial NetworksPreprint

Chen, T., Zhai, X., Ritter, M., Lučić, M., & Houlsby, N. (2018) · arXiv (Cornell University) · Cited by 15 · see citing works

Enhanced self-supervised GANs with blend ratio classification

Zhou, Q., Zhang, J., Han, G., Ruan, Z., & Wei, Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12056-2 · Cited by 3 · see citing works

Transformation GAN for Unsupervised Image Synthesis and Representation Learning

Wang, J., Zhou, W., Qi, G.-J., Fu, Z., Tian, Q. C., & Li, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00055 · Cited by 30 · see citing works

Latent Space Conditioning on Generative Adversarial Networks

Durall, R., Ho, K., Pfreundt, F., & Keuper, J. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010178800240034

An Improved Self-supervised GAN via Adversarial TrainingPreprint

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