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Self-supervised learning (SSL) aims to produce useful feature representations without access to any human-labeled data annotations.
Visualizing data using t-sne
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Adam: A method for stochastic optimization
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Tiny imagenet visual recognition challenge
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Deep residual learning for image recognition
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Momentum contrast for unsupervised visual representation learning
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Self-supervised visual feature learning with deep neural networks: A survey
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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A simple framework for contrastive learning of visual representations
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Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Intriguing properties of contrastive losses
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, and Stefanie Jegelka · 2020
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Whitening for Self-Supervised Representation Learning
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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, et al · 2020
Exploring simple siamese representation learning
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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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
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2021
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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BYOL works even without batch statistics
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Self-supervised learning from a multi-view perspective
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Tongzhou Wang and Phillip Isola · 2020
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
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Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2021
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Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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Understanding self-supervised Learning Dynamics without Contrastive Pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
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