Revisiting the Critical Factors of Augmentation-Invariant Representation
Learning

Revisiting the Critical Factors of Augmentation-Invariant Representation Learning

30 July 2022

Xiangyu Zhang

Papers citing "Revisiting the Critical Factors of Augmentation-Invariant Representation Learning"

9 / 9 papers shown

Title
Pixel-Wise Contrastive Distillation Junqiang Huang Zichao Guo 42 4 0 01 Nov 2022
Minimalistic Unsupervised Learning with the Sparse Manifold Transform Yubei Chen Zeyu Yun Y. Ma Bruno A. Olshausen Yann LeCun 52 8 0 30 Sep 2022
On Feature Decorrelation in Self-Supervised Learning Tianyu Hua Wenxiao Wang Zihui Xue Sucheng Ren Yue Wang Hang Zhao SSL OOD 124 187 0 02 May 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 317 5,785 0 29 Apr 2021
Understanding self-supervised Learning Dynamics without Contrastive Pairs Yuandong Tian Xinlei Chen Surya Ganguli SSL 138 279 0 12 Feb 2021
BYOL works even without batch statistics Pierre Harvey Richemond Jean-Bastien Grill Florent Altché Corentin Tallec Florian Strub ... Samuel L. Smith Soham De Razvan Pascanu Bilal Piot Michal Valko SSL 250 114 0 20 Oct 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 267 3,371 0 09 Mar 2020
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 261 1,275 0 06 Mar 2017
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 39,198 0 01 Sep 2014