Fetching the paper…
Reading the bibliography…
Self-supervised visual representation learning aims to learn useful representations without relying on human annotations.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and M. Douze · 2018
Earlier work this paper cites.
Implicit regularization in matrix factorization
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nathan Srebro · 2018
Earlier work this paper cites.
The implicit bias of gradient descent on separable data
Daniel Soudry, E. Hoffer, Suriya Gunasekar, and Nathan Srebro · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aäron van den Oord, Y. Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Mario Lezcano Casado and David Martínez-Rubio · 2019
Earlier work this paper cites.
Gradient descent aligns the layers of deep linear networks
Ziwei Ji and Matus Telgarsky · 2019
Earlier work this paper cites.
Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Y. LeCun, and Nathan Srebro · 2019
Earlier work this paper cites.
A mathematical theory of semantic development in deep neural networks
Andrew M. Saxe, James L. McClelland, and S. Ganguli · 2019
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
Cited alongside, same era.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
Cited alongside, same era.
Implicit rank-minimizing autoencoder
L. Jing, J. Zbontar, and Y. LeCun · 2020
Cited alongside, same era.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, J. Ponce, and Y. LeCun · 2021
Closest in time.
Implicit gradient regularization
D. Barrett and B. Dherin · 2021
Closest in time.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Herv’e J’egou, J. Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Closest in time.
With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2021
Closest in time.
Provable guarantees for self-supervised deep learning with spectral contrastive loss
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo · 2020
Cited alongside, same era.
Self-supervised learning of pretext-invariant representations
Ishan Misra and L. V. D. Maaten · 2020
Cited alongside, same era.
On alignment in deep linear neural networks
Adityanarayanan Radhakrishnan, Eshaan Nichani, D. Bernstein, and Caroline Uhler · 2020
Cited alongside, same era.
Understanding self-supervised learning with dual deep networks
Yuandong Tian, Lantao Yu, Xinlei Chen, and Surya Ganguli · 2020
Cited alongside, same era.
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, and Michael G. Rabbat · 2021
Cited alongside, same era.
Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, W. Hu, and Yuping Luo
Cited in the paper.
A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, H. Khandeparkar, M. Khodak, Orestis Plevrakis, and Nikunj Saunshi
Cited in the paper.
Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
Closest in time.
On feature decorrelation in self-supervised learning
Tianyu Hua, Wenxiao Wang, Zihui Xue, Yue Wang, Sucheng Ren, and Hang Zhao · 2021
Closest in time.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, R. Socher, and S. Hoi · 2021
Closest in time.
Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and S. Ganguli · 2021
Closest in time.
Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, A. Krishnamurthy, and Daniel J. Hsu · 2021
Closest in time.
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Closest in time.