Self-Supervised Feature Learning by Learning to Spot Artifacts

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Unsupervised representation learning with deep convolutional generative adversarial networks

Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works

Improved Techniques for Training GANs

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · see citing works

Context Encoders: Feature Learning by Inpainting

Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691 · see citing works

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Colorful Image Colorization

Zhang, R., Isola, P., & Efros, A. A. (2016) · European Conference on Computer Vision · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,803 · see citing works

Image inpainting

Bertalmı́o, M., Sapiro, G., Caselles, V., & Ballester, C. (2000) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/344779.344972 · Cited by 3,747 · see citing works

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works

An analysis of single-layer networks in unsupervised feature learning

Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,760 · see citing works

Unsupervised Visual Representation Learning by Context Prediction

Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629 · see citing works

Untitled work

(2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2,534 · see citing works

Adversarial Feature LearningPreprint

Donahue, J., Krähenbühl, P., & Darrell, T. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1605.09782 · Cited by 1,982 · see citing works

Learning from Simulated and Unsupervised Images through Adversarial Training

Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017) · 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.2017.241 · Cited by 1,831 · see citing works

Understanding Neural Networks Through Deep VisualizationPreprint

Yosinski, J., Clune, J., Nguyen, A. M., Fuchs, T. J., & Lipson, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06579 · Cited by 1,687 · see citing works

Adversarial Autoencoders

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv · doi:10.48550/arxiv.1511.05644 · Cited by 1,579 · see citing works

Untitled work

(1993) · Cited by 1,173 · see citing works

Unsupervised Learning of Visual Representations Using Videos

Wang, X., & Gupta, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.320 · Cited by 1,005 · see citing works

Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksPreprint

Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., & Brox, T. (2014) · arXiv (Cornell University) · Cited by 790 · see citing works

Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

Misra, I., Zitnick, C. L., & Hebert, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_32 · Cited by 779 · see citing works

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

Zhang, R., Isola, P., & Efros, A. A. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.76 · Cited by 724 · see citing works

Learning to See by Moving

Agrawal, P., Carreira, J., & Malik, J. (2015) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2015.13 · Cited by 639 · see citing works

Learning Features by Watching Objects Move

Pathak, D., Girshick, R. B., Dollár, P., Darrell, T., & Hariharan, B. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.638 · Cited by 549 · see citing works

Colorization as a Proxy Task for Visual Understanding

Larsson, G., Maire, M., & Shakhnarovich, G. (2017) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.96 · Cited by 527 · see citing works

Multi-Task Bayesian Optimization

Swersky, K., Snoek, J., & Adams, R. P. (2013) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 485 · see citing works

Ambient Sound Provides Supervision for Visual Learning

Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., & Torralba, A. B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_48 · Cited by 385 · see citing works

Representation Learning by Learning to Count

Noroozi, M., Pirsiavash, H., & Favaro, P. (2017) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2017.628 · Cited by 379 · see citing works

Learning Image Representations Tied to Ego-Motion

Jayaraman, D., & Grauman, K. L. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.166 · Cited by 229 · see citing works

Unsupervised Learning by Predicting Noise

Bojanowski, P., & Joulin, A. (2017) · arXiv · doi:10.48550/arxiv.1704.05310 · Cited by 228 · see citing works

Stacked What-Where Auto-encodersPreprint

Zhao, J., Mathieu, M., Goroshin, R., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.02351 · Cited by 216 · see citing works

Data-dependent Initializations of Convolutional Neural NetworksPreprint

Krähenbühl, P., Doersch, C., Donahue, J., & Darrell, T. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.06856 · Cited by 211 · see citing works

The Curious Robot: Learning Visual Representations via Physical Interactions

Pinto, L., Gandhi, D., Han, Y., Park, Y., & Gupta, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_1 · Cited by 160 · see citing works

Semi-Supervised Learning with Context-Conditional Generative Adversarial NetworksPreprint

Denton, E., Gross, S., & Fergus, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06430 · Cited by 130 · see citing works

Unsupervised Learning of Discriminative Attributes and Visual Representations

Huang, C., Loy, C. C., & Tang, X. (2016) · 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.2016.559 · Cited by 99 · see citing works

Convolutional Clustering for Unsupervised LearningPreprint

Dündar, A., Jin, J., & Culurciello, E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06241 · Cited by 72 · see citing works

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499 · 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

SCAN: Learning to Classify Images Without Labels

Gansbeke, W. V., Vandenhende, S., Georgoulis, S., Proesmans, M., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_16 · Cited by 561 · see citing works

SELF-LABELLING VIA SIMULTANEOUS CLUSTERING AND REPRESENTATION LEARNINGPreprint

Asano, Y. M., Rupprecht, C., & Vedaldi, A. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1911.05371 · Cited by 450 · see citing works

Local Aggregation for Unsupervised Learning of Visual Embeddings

Zhuang, C., Zhai, A., & Yamins, D. L. K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00610 · Cited by 422 · see citing works

Self-Supervised Learning in Remote Sensing: A review

Wang, Y., Albrecht, C. M., Braham, N. A. A., Mou, L., & Zhu, X. X. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3198244 · Cited by 364 · see citing works

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed, A., Ozcelebi, T., & Lukkien, J. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3328932 · Cited by 346 · see citing works

Unsupervised Pre-Training of Image Features on Non-Curated Data

Caron, M., Bojanowski, P., Mairal, J., & Joulin, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00305 · Cited by 258 · see citing works

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals

Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231 · see citing works

Self-Supervised Representation Learning by Rotation Feature Decoupling

Feng, Z., Xu, C., & Tao, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01061 · Cited by 225 · see citing works

Online Deep Clustering for Unsupervised Representation Learning

Zhan, X., Xie, J., Liu, Z., Ong, Y.-S., & Loy, C. C. (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/cvpr42600.2020.00672 · Cited by 204 · see citing works

Attribute Restoration Framework for Anomaly Detection

Ye, F., Huang, C., Cao, J., Li, M., Zhang, Y., & Lu, C. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3046884 · Cited by 203 · see citing works

Unsupervised Multi-Task Feature Learning on Point Clouds

Hassani, K., & Haley, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00825 · Cited by 198 · see citing works

Video Representation Learning by Recognizing Temporal Transformations

Jenni, S., Meishvili, G., & Favaro, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_26 · Cited by 126 · see citing works

Learning Visual Representations with Caption Annotations

Sarıyıldız, M. B., Pérez, J., & Larlus, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_10 · Cited by 120 · see citing works

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Huang, C., Xu, Q., Wang, Y.-F., Wang, Y., & Zhang, Y. (2022) · IEEE transactions on multimedia · doi:10.1109/tmm.2022.3175611 · Cited by 110 · see citing works

A critical analysis of self-supervision, or what we can learn from a single image

Asano, Y. M., Rupprecht, C., & Vedaldi, A. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1904.13132 · Cited by 115 · see citing works

Self-Supervised Learning of Point Clouds via Orientation Estimation

Poursaeed, O., Jiang, T., Qiao, H., Xu, N., & Kim, V. G. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00112 · Cited by 113 · 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

Almost Unsupervised Learning for Dense Crowd Counting

Sam, D. B., Sajjan, N. N., Maurya, H., & Babu, R. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018868 · Cited by 102 · see citing works

Self-Supervised Deep Domain-Adversarial Regression Adaptation for Online Remaining Useful Life Prediction of Rolling Bearing Under Unknown Working Condition

Mao, W., Chen, J., Liu, J., & Liang, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3172704 · Cited by 101 · see citing works

Self-Supervised Learning of Object Parts for Semantic Segmentation

Ziegler, A., & Asano, Y. M. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.01410 · Cited by 160 · see citing works

SCARF: Self-Supervised Contrastive Learning using Random Feature CorruptionPreprint

Bahri, D., Jiang, H., Tay, Y., & Metzler, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15147 · Cited by 99 · see citing works

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Lu, A. X., Kraus, O. Z., Cooper, S., & Moses, A. M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007348 · Cited by 94 · see citing works

Large-Scale Unsupervised Semantic Segmentation

Gao, S., Li, Z.-Y., Yang, M.-H., Cheng, M.-M., Han, J., & Torr, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218275 · Cited by 88 · see citing works

Geographical Knowledge-Driven Representation Learning for Remote Sensing Images

Li, W., Chen, K., Chen, H., & Shi, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3115569 · Cited by 86 · 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

Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination

Wang, X., Liu, Z., & Yu, S. X. (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/cvpr46437.2021.01240 · Cited by 81 · see citing works

Improving Object Detection with Selective Self-supervised Self-training

Li, Y., Huang, D., Qin, D., Wang, L., & Gong, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_35 · Cited by 78 · see citing works

Jigsaw Clustering for Unsupervised Visual Representation Learning

Chen, P., Liu, S., & Jia, J. (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/cvpr46437.2021.01136 · Cited by 77 · see citing works

SiT: Self-supervised vIsion Transformer

Atito, S., Awais, M., & Kittler, J. (2021) · arXiv · doi:10.48550/arxiv.2104.03602 · Cited by 75 · see citing works

Self-EMD: Self-Supervised Object Detection without ImageNetPreprint

Liu, S., Li, Z., & Sun, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13677 · Cited by 75 · see citing works

Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts

Kolides, A., Nawaz, A., Rathor, A., Beeman, D., Hashmi, M., Fatima, S., Berdik, D., Al‐Ayyoub, M., & Jararweh, Y. (2023) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2023.102754 · Cited by 73 · see citing works

MixCo: Mix-up Contrastive Learning for Visual RepresentationPreprint

Kim, S., Lee, G., Bae, S., & Yun, S.-Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06300 · Cited by 64 · see citing works

Semantic Segmentation of Remote Sensing Images With Self-Supervised Multitask Representation Learning

Li, W., Chen, H., & Shi, Z. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3090418 · Cited by 63 · see citing works

Cross Pixel Optical-Flow Similarity for Self-supervised Learning

Mahendran, A., Thewlis, J., & Vedaldi, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_7 · Cited by 59 · see citing works

A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification

Ren, B., Zhao, Y., Hou, B., Chanussot, J., & Jiao, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3048967 · Cited by 56 · see citing works

TCGAN: Convolutional Generative Adversarial Network for time series classification and clustering

Huang, F., & Deng, Y. S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.06.033 · Cited by 56 · see citing works

Vi²CLR: Video and Image for Visual Contrastive Learning of Representation

Diba, A., Sharma, V., Safdari, R., Lotfi, D., Sarfraz, M. S., Stiefelhagen, R., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00153 · Cited by 53 · see citing works

Time-Equivariant Contrastive Video Representation Learning

Jenni, S., & Jin, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00982 · Cited by 53 · see citing works

Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations

Lee, W., Na, J., & Kim, G. (2019) · 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.2019.00512 · Cited by 52 · see citing works

CompRess: Self-Supervised Learning by Compressing RepresentationsPreprint

Koohpayegani, S. A., Tejankar, A., & Pirsiavash, H. (2020) · arXiv (Cornell University) · doi:10.13016/m2n1nd-9amp · Cited by 52 · see citing works

Visual Permutation Learning

Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2873701 · Cited by 51 · see citing works

Self-Supervised Learning via Conditional Motion Propagation

Zhan, X., Pan, X., Liu, Z., Lin, D., & Loy, C. C. (2019) · 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.2019.00198 · Cited by 50 · see citing works

Steel surface defect detection based on self-supervised contrastive representation learning with matching metric

Hu, X., Yang, J., Jiang, F., Hussain, A., Dashtipour, K. K., & Gogate, M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110578 · Cited by 49 · see citing works

Self-Supervised Representation Learning From Multi-Domain Data

Feng, Z., Xu, C., & Tao, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00334 · Cited by 48 · see citing works

Automatic Shortcut Removal for Self-Supervised Representation LearningPreprint

Minderer, M., Bachem, O., Houlsby, N., & Tschannen, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08822 · Cited by 48 · see citing works

Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains

Senanayaka, J. S. L., Khang, H. V., & Robbersmyr, K. G. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3014422 · Cited by 45 · see citing works

Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics

Jenni, S., Jin, H., & Favaro, P. (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/cvpr42600.2020.00644 · Cited by 44 · see citing works

Self-supervised transfer learning of physiological representations from free-living wearable data

Spathis, D., Perez-Pozuelo, I., Brage, S., Wareham, N. J., & Mascolo, C. (2021) · Proceedings of the Conference on Health, Inference, and Learning · doi:10.1145/3450439.3451863 · Cited by 40 · see citing works

Self-Supervised Relational Reasoning for Representation LearningPreprint

Patacchiola, M., & Storkey, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05849 · Cited by 39 · see citing works

Self-supervised-RCNN for medical image segmentation with limited data annotation

Felfeliyan, B., Forkert, N. D., Hareendranathan, A. R., Cornel, D., Zhou, Y., Kuntze, G., Jaremko, J. L., & Ronsky, J. L. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102297 · Cited by 38 · see citing works

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

Sabokrou, M., Khalooei, M., & Adeli, E. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00810 · Cited by 38 · see citing works

An Overview of Vision Transformers for Image Processing: A Survey

Kameswari, C. S., Kavitha, J., Reddy, T. S., Chinthaguntla, B., Jagatheesaperumal, S. K., Gaftandzhieva, S. N., & Doneva, R. Z. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140830 · Cited by 37 · see citing works

Fully Convolutional Network-Based Self-Supervised Learning for Semantic Segmentation

Yang, Z.-G., Yu, H.-S., He, Y., Sun, W., Mao, Z.-H., & Mian, A. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3172423 · Cited by 32 · see citing works

Completely Self-supervised Crowd Counting via Distribution Matching

Sam, D. B., Agarwalla, A., Joseph, J., Sindagi, V. A., Babu, R. V., & Patel, V. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_11 · Cited by 29 · see citing works

Bringing Generalization to Deep Multi-View Pedestrian Detection

Vora, J., Dutta, S., Jain, K., Karthik, S., & Gandhi, V. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw58289.2023.00016 · Cited by 28 · see citing works

Learning Actionness via Long-Range Temporal Order Verification

Zhukov, D., Alayrac, J.-B., Laptev, I., & Šivic, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_28 · Cited by 22 · see citing works

Audio-Visual Contrastive Learning with Temporal Self-Supervision

Jenni, S., Black, A., & Collomosse, J. P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25967 · Cited by 21 · see citing works

ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets

Kinakh, V., Taran, O., & Voloshynovskiy, S. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00129 · Cited by 18 · see citing works

Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications

Yang, Z., Du, H., Niyato, D. T., Wang, X., Zhou, Y. F., Feng, L., Zhou, F., Li, W., & Qiu, X. (2025) · IEEE Wireless Communications · doi:10.1109/mwc.002.2400197 · Cited by 18 · see citing works

Point-Guided Contrastive Learning for Monocular 3-D Object Detection

Feng, D., Han, S., Xu, H., Liang, X., & Tan, X. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3090370 · Cited by 17 · see citing works

Self-supervised Learning for Reading Activity Classification

Islam, M. R., Sakamoto, S., Yamada, Y., Vargo, A. W., Iwata, M., Iwamura, M., & Kise, K. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478088 · Cited by 17 · see citing works

Geographical Supervision Correction for Remote Sensing Representation Learning

Li, W., Chen, K., & Shi, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3202499 · Cited by 16 · see citing works

PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data

Razdaibiedina, A., Brechalov, A. V., Friesen, H., Ušaj, M. M., Masinas, M. P. D., Suresh, H. G., Wang, K. E., Boone, C. M., Ba, J. L., & Andrews, B. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00029-6 · Cited by 15 · see citing works

Deep Image Comparator: Learning to Visualize Editorial Change

Black, A., Bui, T., Jin, H., Swaminathan, V., & Collomosse, J. P. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00108 · Cited by 13 · see citing works

Asymmetric Supervised Deep Autoencoder for Depth Image based 3D Model Retrieval

Siddiqua, A., & Fan, G. (2019) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip47243.2019.8965682 · Cited by 12 · see citing works

Global-and-Local sampling for efficient hybrid task self-supervised learning

Zhao, W., Xu, Y., Li, L., & Yang, H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110479 · Cited by 12 · see citing works

Unsupervised Feature Learning by Cross-Level Discrimination between Instances and GroupsPreprint

Wang, X., Liu, Z., & Yu, S. X. (2020) · arXiv (Cornell University) · Cited by 12 · see citing works

Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers

Wu, Y., Liu, J., Wang, Y., Gibson, S. J., Osadchy, M., & Fang, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3347617 · Cited by 11 · see citing works

No More Shortcuts: Realizing the Potential of Temporal Self-Supervision

Dave, I. R., Jenni, S., & Shah, M. A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27913 · Cited by 10 · see citing works

Self-supervised blur detection from synthetically blurred scenes

Álvarez-Gila, A., Galdrán, A., Garrote, E., & van de Weijer, J. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.08.008 · Cited by 10 · see citing works

MC-SSL0.0: Towards Multi-Concept Self-Supervised LearningPreprint

Atito, S., Awais, M., Farooq, A., Feng, Z.-H., & Kittler, J. (2021) · arXiv.org · doi:10.48550/arxiv.2111.15340 · Cited by 19 · see citing works

Progress and Thinking on Self-Supervised Learning Methods in Computer Vision: A Review

Chen, Z., Hu, B., Chen, Z., & Zhang, J. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3443885 · Cited by 8 · see citing works

Deep self‐supervised transformation learning for leukocyte classification

Chen, X., Zheng, G., Zhou, L., Li, Z.-Y., & Fan, H. (2022) · Journal of Biophotonics · doi:10.1002/jbio.202200244 · Cited by 8 · see citing works

Multi-pretraining for Large-scale Text Classification

Kim, K.-M., Hyeon, B.-S., Kim, Y., Park, J.-H., & Lee, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.185 · Cited by 8 · see citing works

Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image TransformationsPreprint

Yamaguchi, S., Kanai, S., Shioda, T., & Takeda, S. (2019) · arXiv (Cornell University) · Cited by 8 · see citing works

Learning the Precise Feature for Cluster Assignment

Gan, Y., Dong, X., Zhou, H., Gao, F., & Dong, J. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3079914 · Cited by 7 · see citing works

Unsupervised Learning of Landmarks by Descriptor Vector Exchange

Thewlis, J., Albanie, S., Bilen, H., & Vedaldi, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00646 · Cited by 7 · see citing works

Surprising Effectiveness of Few-Image Unsupervised Feature Learning.Preprint

Asano, Y. M., Rupprecht, C., & Vedaldi, A. (2019) · arXiv (Cornell University) · Cited by 7 · see citing works

Enhancing Visual Embeddings through Weakly Supervised Captioning for Zero-Shot Learning

Bustreo, M., Cavazza, J., & Murino, V. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00165 · Cited by 6 · see citing works

Robust RGB-based 6-DoF Pose Estimation without Real Pose AnnotationsPreprint

Li, Z., Hu, Y., Salzmann, M., & Ji, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08391 · Cited by 6 · see citing works

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

Yang, Z., Ding, K., Liu, H., & Wang, Y. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00327 · Cited by 5 · see citing works

Self-supervised Representation Learning Using 360° Data

Li, J., Liu, J., Wong, Y., Nishimura, S., & Kankanhalli, M. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351019 · Cited by 5 · see citing works

Self-Supervised Health Index Curve Generation for Condition-Based Predictive Maintenance

Seitz, S., Arnold, M., Tetzlaff, R., & Holstein, P. (2023) · Electronics · doi:10.3390/electronics12244941 · Cited by 4 · see citing works

Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama

Mei, L., He, Y., Fishani, F. J., Yu, Y., Zhang, L., & Rhodin, H. (2023) · Remote Sensing · doi:10.3390/rs15040953 · Cited by 3 · see citing works

Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images

Li, Q., Li, B., Garibaldi, J. M., & Qiu, G. (2022) · Remote Sensing · doi:10.3390/rs14143361 · Cited by 3 · see citing works

Refining Self-Supervised Learning in Imaging: Beyond Linear Metric

Jiang, B., Krim, H., Wu, T., & Cansever, D. H. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897745 · Cited by 3 · see citing works

Overcoming Data Scarcity for Coronary Vessel Segmentation Through Self-supervised Pre-training

Kraft, M., Pieczyński, D., & Siemionow, K. ‘. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92238-2_31 · Cited by 3 · see citing works

Learnable Masked Tokens for Improved Transferability of Self-supervised Vision Transformers

Hu, H., Baldassarre, F., & Azizpour, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26409-2_25 · Cited by 3 · see citing works

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

Liu, Y., Huang, L., Pan, P., Wang, B., Xu, Y., & Jin, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17055 · Cited by 3 · see citing works

VCL-PL:Semi-Supervised Learning from Noisy Web Data with Variational Contrastive Learning

Yavuz, M. C., & Yanikoglu, B. A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956152 · Cited by 2 · see citing works

Representation Learning by Detecting Incorrect Location Embeddings

Sameni, S., Jenni, S., & Favaro, P. (2022) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26160 · Cited by 9 · see citing works

Detecting abnormal cell behaviors from dry mass time series

Bailly, R., Malfante, M., Allier, C. P., Paviolo, C., Ghenim, L., Padmanabhan, K., Bardin, S., & Mars, J. I. (2024) · Scientific Reports · doi:10.1038/s41598-024-57684-w · Cited by 2 · see citing works

Unsupervised Part Discovery via Dual Representation Alignment

Xia, J., Huang, W., Xu, M. G., Zhang, J., Zhang, H., Sheng, Z., & Xu, D. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3445582 · Cited by 2 · see citing works

DETECTren: Vehicle Object Detection Using Self-Supervised Learning based on Light-Weight Network for Low-Power Devices

Fuhrmann, H., Boyko, A., Abdelpakey, M. H., & Shehata, M. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9594927 · Cited by 2 · see citing works

Unsupervised Fashion Style Learning by Solving Fashion Jigsaw Puzzles

Chen, J., Yuan, H., Fang, F., Peng, T., & Hu, X. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00317 · Cited by 1 · see citing works

Potential Deep Learning Solutions to Persistent and Emerging Big Data Challenges—A Practitioners’ Cookbook

Mirza, B., Syed, T., Khan, B., & Malik, Y. (2021) · ACM Computing Surveys · doi:10.1145/3427476 · Cited by 1 · see citing works

Monocular three-dimensional object detection using data augmentation and self-supervised learning in autonomous driving

Thayalan, S., Srıdevı, M., Santhakumar, K., Kiran, B. R., Liu, H., Gauthier, T., & Yogamani, S. K. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.1.011004 · Cited by 1 · see citing works

Improving Image Clustering through Sample Ranking and Its Application to Remote Sensing Images

Li, Q., & Qiu, G. (2022) · Remote Sensing · doi:10.3390/rs14143317 · Cited by 1 · see citing works

Adversarial Robustness of Discriminative Self-Supervised Learning in Vision

Çağatan, Ö. V., Tal, O. F., & Gürsoy, M. E. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00223 · Cited by 1 · see citing works

Inverting Adversarially Robust Networks for Image Synthesis

Rojas-Gómez, R. A., Yeh, R. A., Do, M. N., & Nguyen, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26351-4_24 · Cited by 3 · see citing works

Self-supervised Representation Learning for Adversarial Attack Detection

Li, Y., Angelov, P. P., & Suri, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73027-6_14 · Cited by 11 · see citing works

Enhancing self-supervised visual representation learning through adversarially generated examples

Kang, M., & Kim, J. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11236-z · Cited by 1 · see citing works

Learning-aware feature denoising discriminator

Gan, Y., Xiang, T., Liu, H., & Ye, M. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.006 · Cited by 15 · see citing works