On the surprising similarities between supervised and self-supervised
models

On the surprising similarities between supervised and self-supervised models

16 October 2020

Kantharaju Narayanappa

Benjamin Mitzkus

Matthias Bethge

Wieland Brendel

Papers citing "On the surprising similarities between supervised and self-supervised models"

13 / 13 papers shown

Title
Trapped in texture bias? A large scale comparison of deep instance segmentation J. Theodoridis Jessica Hofmann J. Maucher A. Schilling SSeg 27 5 0 17 Jan 2024
Improving neural network representations using human similarity judgments Lukas Muttenthaler Lorenz Linhardt Jonas Dippel Robert A. Vandermeulen Katherine L. Hermann Andrew Kyle Lampinen Simon Kornblith 40 29 0 07 Jun 2023
On the Stepwise Nature of Self-Supervised Learning James B. Simon Maksis Knutins Liu Ziyin Daniel Geisz Abraham J. Fetterman Joshua Albrecht SSL 32 29 0 27 Mar 2023
The Robustness Limits of SoTA Vision Models to Natural Variation Mark Ibrahim Q. Garrido Ari S. Morcos Diane Bouchacourt VLM 38 16 0 24 Oct 2022
Robust Self-Supervised Learning with Lie Groups Mark Ibrahim Diane Bouchacourt Ari S. Morcos SSL OOD 38 6 0 24 Oct 2022
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility Rohit Gupta Naveed Akhtar Ajmal Saeed Mian M. Shah AAML SSL 21 5 0 22 Jul 2022
Assessing the State of Self-Supervised Human Activity Recognition using Wearables H. Haresamudram Irfan Essa Thomas Plötz SSL 34 85 0 22 Feb 2022
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation Binxu Wang David Mayo Arturo Deza Andrei Barbu C. Conwell 31 12 0 14 Dec 2021
Revisiting Model Stitching to Compare Neural Representations Yamini Bansal Preetum Nakkiran Boaz Barak FedML 29 104 0 14 Jun 2021
Partial success in closing the gap between human and machine vision Robert Geirhos Kantharaju Narayanappa Benjamin Mitzkus Tizian Thieringer Matthias Bethge Felix Wichmann Wieland Brendel VLM AAML 42 221 0 14 Jun 2021
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations Chaitanya K. Ryali D. Schwab Ari S. Morcos SSL 32 9 0 23 Mar 2021
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency Robert Geirhos Kristof Meding Felix Wichmann 11 116 0 30 Jun 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 267 3,369 0 09 Mar 2020