139 citations · 170 across the 2 of their papers we have counts for
5 papers
Self-supervised Pretraining of Visual Features in the Wild
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux +8
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment,…
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Ishan Misra, Julien Mairal +3
Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contr…
Pruning Convolutional Neural Networks with Self-Supervision
Mathilde Caron, Ari Morcos, Piotr Bojanowski +2
Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of par…
Unsupervised Pre-Training of Image Features on Non-Curated Data
Mathilde Caron, Piotr Bojanowski, Julien Mairal +1
Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervi…
Deep Clustering for Unsupervised Learning of Visual Features
Mathilde Caron, Piotr Bojanowski, Armand Joulin +1
Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end tra…