13 citations · 21 across the 2 of their papers we have counts for
8 papers
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…
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Mahmoud Assran, Mathilde Caron, Ishan Misra +4
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures t…
A Closer Look at Codistillation for Distributed Training
Shagun Sodhani, Olivier Delalleau, Mahmoud Assran +3
Codistillation has been proposed as a mechanism to share knowledge among concurrently trained models by encouraging them to represent the same function through an auxiliary loss. T…
Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations
Mahmoud Assran, Nicolas Ballas, Lluis Castrejon +1
We investigate a strategy for improving the efficiency of contrastive learning of visual representations by leveraging a small amount of supervised information during pre-training.…
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
Mahmoud Assran, Michael Rabbat
We study Nesterov's accelerated gradient method with constant step-size and momentum parameters in the stochastic approximation setting (unbiased gradients with bounded variance) a…