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Toward Understanding the Feature Learning Process of Self-supervised
  Contrastive Learning

Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning

31 May 2021
Zixin Wen
Yuanzhi Li
    SSL
    MLT
ArXivPDFHTML

Papers citing "Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning"

28 / 28 papers shown
Title
A Probabilistic Model for Self-Supervised Learning
A Probabilistic Model for Self-Supervised Learning
Maximilian Fleissner
P. Esser
D. Ghoshdastidar
SSL
BDL
93
1
0
22 Jan 2025
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
Jingyang Li
Jiachun Pan
Vincent Y. F. Tan
Kim-Chuan Toh
Pan Zhou
AAML
MLT
43
0
0
15 Oct 2024
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Binghui Li
Yuanzhi Li
OOD
28
2
0
11 Oct 2024
InfoNCE: Identifying the Gap Between Theory and Practice
InfoNCE: Identifying the Gap Between Theory and Practice
E. Rusak
Patrik Reizinger
Attila Juhos
Oliver Bringmann
Roland S. Zimmermann
Wieland Brendel
33
5
0
28 Jun 2024
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for
  Continual Learning
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning
Prashant Bhat
Bharath Renjith
Elahe Arani
Bahram Zonooz
CLL
40
2
0
28 Apr 2024
Complexity Matters: Dynamics of Feature Learning in the Presence of
  Spurious Correlations
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations
GuanWen Qiu
Da Kuang
Surbhi Goel
25
8
0
05 Mar 2024
Better Representations via Adversarial Training in Pre-Training: A
  Theoretical Perspective
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Yue Xing
Xiaofeng Lin
Qifan Song
Yi Tian Xu
Belinda Zeng
Guang Cheng
SSL
15
0
0
26 Jan 2024
Unraveling Projection Heads in Contrastive Learning: Insights from
  Expansion and Shrinkage
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage
Yu Gui
Cong Ma
Yiqiao Zhong
19
6
0
06 Jun 2023
On the Stepwise Nature of Self-Supervised Learning
On the Stepwise Nature of Self-Supervised Learning
James B. Simon
Maksis Knutins
Liu Ziyin
Daniel Geisz
Abraham J. Fetterman
Joshua Albrecht
SSL
29
29
0
27 Mar 2023
A Theoretical Understanding of Shallow Vision Transformers: Learning,
  Generalization, and Sample Complexity
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity
Hongkang Li
M. Wang
Sijia Liu
Pin-Yu Chen
ViT
MLT
35
56
0
12 Feb 2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien A. Cabannes
B. Kiani
Randall Balestriero
Yann LeCun
A. Bietti
SSL
11
31
0
06 Feb 2023
Deciphering the Projection Head: Representation Evaluation
  Self-supervised Learning
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning
Jiajun Ma
Tianyang Hu
Wenjia Wang
17
8
0
28 Jan 2023
Augmentation Invariant Manifold Learning
Augmentation Invariant Manifold Learning
Shulei Wang
29
1
0
01 Nov 2022
The Curious Case of Benign Memorization
The Curious Case of Benign Memorization
Sotiris Anagnostidis
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
AAML
41
8
0
25 Oct 2022
A Comprehensive Survey of Data Augmentation in Visual Reinforcement
  Learning
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Guozheng Ma
Zhen Wang
Zhecheng Yuan
Xueqian Wang
Bo Yuan
Dacheng Tao
OffRL
30
26
0
10 Oct 2022
Joint Embedding Self-Supervised Learning in the Kernel Regime
Joint Embedding Self-Supervised Learning in the Kernel Regime
B. Kiani
Randall Balestriero
Yubei Chen
S. Lloyd
Yann LeCun
SSL
41
13
0
29 Sep 2022
Towards Understanding Mixture of Experts in Deep Learning
Towards Understanding Mixture of Experts in Deep Learning
Zixiang Chen
Yihe Deng
Yue-bo Wu
Quanquan Gu
Yuan-Fang Li
MLT
MoE
27
53
0
04 Aug 2022
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial
  Attacks
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks
Siddhartha Datta
AAML
30
4
0
15 May 2022
The Mechanism of Prediction Head in Non-contrastive Self-supervised
  Learning
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning
Zixin Wen
Yuanzhi Li
SSL
27
34
0
12 May 2022
Empirical Evaluation and Theoretical Analysis for Representation
  Learning: A Survey
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
Kento Nozawa
Issei Sato
AI4TS
16
4
0
18 Apr 2022
Frequency Selective Augmentation for Video Representation Learning
Frequency Selective Augmentation for Video Representation Learning
Jinhyung Kim
Taeoh Kim
Minho Shim
Dongyoon Han
Dongyoon Wee
Junmo Kim
AI4TS
41
3
0
08 Apr 2022
Understanding Contrastive Learning Requires Incorporating Inductive
  Biases
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi
Jordan T. Ash
Surbhi Goel
Dipendra Kumar Misra
Cyril Zhang
Sanjeev Arora
Sham Kakade
A. Krishnamurthy
SSL
19
109
0
28 Feb 2022
Sample Efficiency of Data Augmentation Consistency Regularization
Sample Efficiency of Data Augmentation Consistency Regularization
Shuo Yang
Yijun Dong
Rachel A. Ward
Inderjit S. Dhillon
Sujay Sanghavi
Qi Lei
AAML
23
17
0
24 Feb 2022
Towards the Generalization of Contrastive Self-Supervised Learning
Towards the Generalization of Contrastive Self-Supervised Learning
Weiran Huang
Mingyang Yi
Xuyang Zhao
Zihao Jiang
SSL
21
105
0
01 Nov 2021
Towards Demystifying Representation Learning with Non-contrastive
  Self-supervision
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Xiang Wang
Xinlei Chen
S. Du
Yuandong Tian
SSL
16
26
0
11 Oct 2021
On the Surrogate Gap between Contrastive and Supervised Losses
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao
Yoshihiro Nagano
Kento Nozawa
SSL
UQCV
37
19
0
06 Oct 2021
The Power of Contrast for Feature Learning: A Theoretical Analysis
The Power of Contrast for Feature Learning: A Theoretical Analysis
Wenlong Ji
Zhun Deng
Ryumei Nakada
James Y. Zou
Linjun Zhang
SSL
51
48
0
06 Oct 2021
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
230
31,253
0
16 Jan 2013
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