A comprehensive perspective of contrastive self-supervised learning

References

[1] Hinton G, LeCunn Y, Bengio Y. AAAI’2020 keynotes turing award winners event. https://www.youtube.com/watch?v=UX8OubxsY8w

[2] Jing L, Tian Y. Self-supervised visual feature learning with deep neural networks: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, DOI:10.1109/TPAMI.2020.2992393

[3] So I. Cognitive development in children: piaget development and learning. Journal of Research in Science Teaching, 1964, 2: 176–186

[4] Jaiswal A, Babu A R, Zadeh M Z, Banerjee D, Makedon F. A survey on contrastive self-supervised learning. Technologies, 2021, 9(1): 2

[5] Saunshi N, Plevrakis O, Arora S, Khodak M, Khandeparkar H. A theoretical analysis of contrastive unsupervised representation learning. In: Proceedings of the 36th International Conference on Machine Learning. 2019, 5628–5637

[6] Tsai Y H H, Wu Y, Salakhutdinov R, Morency L P. Self-supervised learning from a multi-view perspective. In: Proceedings of the 8th International Conference on Learning Representations. 2020

[7] Tosh C, Krishnamurthy A, Hsu D. Contrastive learning, multi-view redundancy, and linear models. In: Proceedings of the 32nd International Conference on Algorithmic Learning Theory. 2021, 1179–1206

[8] Wang T, Isola P. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 9929–9939

[9] Wang W, Zhou Z H. Analyzing co-training style algorithms. In: Proceedings of the 18th European Conference on Machine Learning. 2007, 454–465

[10] Pan J S, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2009, 22(10): 1345–1359

RIGHTS & PERMISSIONS

2021 Higher Education Press