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Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems.
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and Composing Robust Features with Denoising Autoencoders. In: Proceedings of the 25th International Conference on Machine Learning. p. 1096–1103. ICML ’08, Association for Computing Machinery, New York, NY, USA (2008). https://doi.org/10.1145/1390156.1390294, https://doi.org/10.1145/1390156.1390294
2008
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Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848
2009
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Doersch, C., Gupta, A., Efros, A.A.: Unsupervised Visual Representation Learning by Context Prediction. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 1422–1430 (2015). https://doi.org/10.1109/ICCV.2015.167
2015
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He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (December 2015)
2015
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Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. pp. 234–241. Springer International Publishing, Cham (2015)
2015
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Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., Collins, R.: UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Medicine 12
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
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Noroozi, M., Favaro, P.: Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. pp. 69–84. Lecture Notes in Computer Science, Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_5
2016
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2016
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2017
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Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M.A., Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V.A., Krishnamurthi, G., Rohé, M.M., Pennec, X., Sermesant, M., Isensee, F., Jäger, P., Maier-Hein, K.H., Full, P.M., Wolf, I., Engelhardt, S., Baumgartner, C.F., Koch, L.M., Wolterink, J.M., Išgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., Jodoin, P.M.: Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Transactions on Medical Imaging 37
2018
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Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018), http://openai-assets.s3.amazonaws.com/research-covers/language-unsupervised/language_understanding_paper.pdf
2018
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Morid, M.A., Borjali, A., Del Fiol, G.: A scoping review of transfer learning research on medical image analysis using ImageNet. Computers in Biology and Medicine 128
2020
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Yakubovskiy, P.: Segmentation Models Pytorch. https://github.com/qubvel/segmentation_models.pytorch (2020)
2020
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Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V., Norouzi, M.: Big Self-Supervised Models Advance Medical Image Classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3478–3488 (October 2021), https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi_Big_Self-Supervised_Models_Advance_Medical_Image_Classification_ICCV_2021_paper.pdf
2021
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Ciga, O., Xu, T., Martel, A.L.: Self supervised contrastive learning for digital histopathology. Machine Learning with Applications p. 100198 (2021)
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423, https://www.aclweb.org/anthology/N19-1423
2019
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Wightman, R.: PyTorch Image Models. https://github.com/rwightman/pytorch-image-models (2019). https://doi.org/10.5281/zenodo.4414861
2019
Cited alongside, same era.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 9912–9924. Curran Associates, Inc. (2020), https://proceedings.neurips.cc/paper/2020/file/70feb62b69f16e0238f741fab228fec2-Paper.pdf
2020
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Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 1597–1607. PMLR (13–18 Jul 2020), http://proceedings.mlr.press/v119/chen20j.html
2020
Cited alongside, same era.
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., Valko, M.: Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 21271–21284. Curran Associates, Inc. (2020), https://proceedings.neurips.cc/paper/2020/file/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf
2020
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He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum Contrast for Unsupervised Visual Representation Learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020), "https://openaccess.thecvf.com/content_CVPR_2020/html/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html"
2020
Cited alongside, same era.
Hénaff, O.J., Srinivas, A., Fauw, J.D., Razavi, A., Doersch, C., Eslami, S.M.A., van den Oord, A.: Data-Efficient Image Recognition with Contrastive Predictive Coding. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 4182–4192. PMLR (13–18 Jul 2020), http://proceedings.mlr.press/v119/henaff20a.html
2020
Cited alongside, same era.
2021
Later among the works it cites.
2021
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Sowrirajan, H., Yang, J., Ng, A.Y., Rajpurkar, P.: MoCo pretraining improves representation and transferability of chest X-ray models. In: Medical Imaging with Deep Learning. pp. 728–744. PMLR (2021)
2021
Later among the works it cites.
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow Twins: Self-Supervised Learning via Redundancy Reduction. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 12310–12320. PMLR (18–24 Jul 2021), https://proceedings.mlr.press/v139/zbontar21a.html
2021
Later among the works it cites.
da Costa, V.G.T., Fini, E., Nabi, M., Sebe, N., Ricci, E.: solo-learn: A Library of Self-supervised Methods for Visual Representation Learning. Journal of Machine Learning Research 23
2022
Closest in time.
Reed, C.J., Yue, X., Nrusimha, A., Ebrahimi, S., Vijaykumar, V., Mao, R., Li, B., Zhang, S., Guillory, D., Metzger, S., Keutzer, K., Darrell, T.: Self-Supervised Pretraining Improves Self-Supervised Pretraining. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 2584–2594 (January 2022), https://openaccess.thecvf.com/content/WACV2022/html/Reed_Self-Supervised_Pretraining_Improves_Self-Supervised_Pretraining_WACV_2022_paper.html
2022
Closest in time.