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Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL).
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2006)
2006
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Belghazi, M.I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Hjelm, R.D., Courville, A.C.: Mutual information neural estimation. In: Proceedings of the International Conference on Machine Learning (ICML) (2018)
2018
Earlier work this paper cites.
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: European Conference on Computer Vision (ECCV) (2018)
2018
Earlier work this paper cites.
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (ICLR) (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Earlier work this paper cites.
Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization. In: International Conference on Learning Representations (ICLR) (2019)
2019
Earlier work this paper cites.
Lugosch, L., Ravanelli, M., Ignoto, P., Tomar, V.S., Bengio, Y.: Speech model pre-training for end-to-end spoken language understanding. In: the Annual Conference of the International Speech Communication Association (InterSpeech) (2019)
2019
Earlier work this paper cites.
Ozair, S., Lynch, C., Bengio, Y., van den Oord, A., Levine, S., Sermanet, P.: Wasserstein dependency measure for representation learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Earlier work this paper cites.
Ye, M., Zhang, X., Yuen, P.C., Chang, S.F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Earlier work this paper cites.
Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: A framework for self-supervised learning of speech representations. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
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: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: Proceedings of the International Conference on Machine Learning (ICML) (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Zhan, X., Xie, J., Liu, Z., Lin, D., Change Loy, C.: OpenSelfSup: Open mmlab self-supervised learning toolbox and benchmark. https://github.com/open-mmlab/openselfsup (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
2021
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2021
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Grill, J., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.Á., Guo, Z., Azar, M.G., Piot, B., Kavukcuoglu, K., Munos, R., Valko, M.: Bootstrap your own latent - A new approach to self-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
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) (2020)
2020
Cited alongside, same era.
Kalantidis, Y., Sariyildiz, M.B., Pion, N., Weinzaepfel, P., Larlus, D.: Hard negative mixing for contrastive learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
Misra, I., Maaten, L.v.d.: Self-supervised learning of pretext-invariant representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Nagrani, A., Chung, J.S., Xie, W., Zisserman, A.: Voxceleb: Large-scale speaker verification in the wild. Comput. Speech Lang. 60
2020
Cited alongside, same era.
Ren, H.: A pytorch implementation of simclr. https://github.com/leftthomas/SimCLR (2020)
2020
Cited alongside, same era.
Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: European Conference on Computer Vision (ECCV) (2020)
2020
Cited alongside, same era.
Chen, X., He, K.: Exploring simple siamese representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Closest in time.
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., Zisserman, A.: With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9588–9597 (2021)
2021
Closest in time.
Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for self-supervised representation learning. In: International Conference on Machine Learning (ICML) (2021)
2021
Closest in time.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR (2021)
2021
Closest in time.
Robinson, J.D., Chuang, C., Sra, S., Jegelka, S.: Contrastive learning with hard negative samples. In: International Conference on Learning Representations (ICLR) (2021)
2021
Closest in time.
Tsai, Y.H., Ma, M.Q., Yang, M., Zhao, H., Morency, L., Salakhutdinov, R.: Self-supervised representation learning with relative predictive coding. In: International Conference on Learning Representations (ICLR) (2021)
2021
Closest in time.
Wang, P.: x-clip. https://github.com/lucidrains/x-clip (2021)
2021
Closest in time.
Wang, X., Liu, Z., Yu, S.X.: Unsupervised feature learning by cross-level instance-group discrimination. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Closest in time.
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: Self-supervised learning via redundancy reduction. In: International Conference on Machine Learning. pp. 12310–12320. PMLR (2021)
2021
Closest in time.