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Augmentations vs Algorithms: What Works in Self-Supervised Learning
8 March 2024
Warren Morningstar
Alex Bijamov
Chris Duvarney
Luke Friedman
N. Kalibhat
Luyang Liu
Philip Mansfield
Renan A. Rojas-Gomez
Karan Singhal
Bradley Green
Sushant Prakash
SSL
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Papers citing
"Augmentations vs Algorithms: What Works in Self-Supervised Learning"
6 / 6 papers shown
Title
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
Patrik Reizinger
Randall Balestriero
David Klindt
Wieland Brendel
40
0
0
17 Apr 2025
Contrastive Learning Via Equivariant Representation
Sifan Song
Jinfeng Wang
Qiaochu Zhao
Xiang Li
Dufan Wu
Angelos Stefanidis
Jionglong Su
S. Kevin Zhou
Quanzheng Li
33
1
0
01 Jun 2024
What Variables Affect Out-Of-Distribution Generalization in Pretrained Models?
Md Yousuf Harun
Kyungbok Lee
Jhair Gallardo
Giri Krishnan
Christopher Kanan
33
3
0
23 May 2024
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
305
7,434
0
11 Nov 2021
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen
Haoqi Fan
Ross B. Girshick
Kaiming He
SSL
267
3,369
0
09 Mar 2020
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
296
39,194
0
01 Sep 2014
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