743 citations · 3k across the 44 of their papers we have counts for
12 papers · 1 filter
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Florian Bordes, Shashank Shekhar, Mark Ibrahim +3
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) prec…
The Robustness Limits of SoTA Vision Models to Natural Variation
Mark Ibrahim, Quentin Garrido, Ari Morcos +1
Recent state-of-the-art vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classificati…
Robust Self-Supervised Learning with Lie Groups
Mark Ibrahim, Diane Bouchacourt, Ari Morcos
Deep learning has led to remarkable advances in computer vision. Even so, today's best models are brittle when presented with variations that differ even slightly from those seen d…
Grounding inductive biases in natural images:invariance stems from variations in data
Diane Bouchacourt, Mark Ibrahim, Ari S. Morcos
To perform well on unseen and potentially out-of-distribution samples, it is desirable for machine learning models to have a predictable response with respect to transformations af…
Width Transfer: On the (In)variance of Width Optimization
Ting-Wu Chin, Diana Marculescu, Ari S. Morcos
Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large c…
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the u…
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt +3
Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially l…
Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab +1
Self-supervised learning has recently begun to rival supervised learning on computer vision tasks. Many of the recent approaches have been based on contrastive instance discriminat…
Analyzing Visual Representations in Embodied Navigation Tasks
Erik Wijmans, Julian Straub, Dhruv Batra +3
Recent advances in deep reinforcement learning require a large amount of training data and generally result in representations that are often over specialized to the target task. I…
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…
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
Erik Wijmans, Abhishek Kadian, Ari Morcos +5
We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is…
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs
Avraham Ruderman, Neil C. Rabinowitz, Ari S. Morcos +1
Many of our core assumptions about how neural networks operate remain empirically untested. One common assumption is that convolutional neural networks need to be stable to small t…