164 citations · 305 across the 20 of their papers we have counts for
9 papers · 1 filter
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class
Mazda Moayeri, Michael Rabbat, Mark Ibrahim +1
Vision-language models enable open-world classification of objects without the need for any retraining. While this zero-shot paradigm marks a significant advance, even today's best…
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…
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?
Megan Richards, Polina Kirichenko, Diane Bouchacourt +1
For more than a decade, researchers have measured progress in object recognition on ImageNet-based generalization benchmarks such as ImageNet-A, -C, and -R. Recent advances in foun…
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
Laura Gustafson, Megan Richards, Melissa Hall +3
Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of…
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Zhiheng Li, Ivan Evtimov, Albert Gordo +5
Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address thi…
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero +7
Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose,…
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…