234 citations · 518 across the 11 of their papers we have counts for
23 papers
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
Théo Moutakanni, Piotr Bojanowski, Guillaume Chassagnon +7
AI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, le…
Co-training Submodels for Visual Recognition
Hugo Touvron, Matthieu Cord, Maxime Oquab +3
We introduce submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth. Given a neural network to be trained, for each sample we…
The Hidden Uniform Cluster Prior in Self-Supervised Learning
Mahmoud Assran, Randall Balestriero, Quentin Duval +6
A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g., SimCLR, VICReg, SwAV, MSN). We show tha…
Masked Siamese Networks for Label-Efficient Learning
Mahmoud Assran, Mathilde Caron, Ishan Misra +6
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containi…
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Priya Goyal, Quentin Duval, Isaac Seessel +5
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the…
XCiT: Cross-Covariance Image Transformers
Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron +8
Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yiel…