Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations

23 March 2021

Papers citing "Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations"

6 / 6 papers shown

Title
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? Nenad Tomašev Ioana Bica Brian McWilliams Lars Buesing Razvan Pascanu Charles Blundell Jovana Mitrović SSL 58 80 0 13 Jan 2022
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 283 5,723 0 29 Apr 2021
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation Golnaz Ghiasi Yin Cui A. Srinivas Rui Qian Tsung-Yi Lin E. D. Cubuk Quoc V. Le Barret Zoph ISeg 223 962 0 13 Dec 2020
On the surprising similarities between supervised and self-supervised models Robert Geirhos Kantharaju Narayanappa Benjamin Mitzkus Matthias Bethge Felix Wichmann Wieland Brendel OOD SSL DRL 66 46 0 16 Oct 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 238 3,359 0 09 Mar 2020
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,813 0 08 Jul 2016