137 citations · 348 across the 31 of their papers we have counts for
7 papers · 1 filter
Understanding the Detrimental Class-level Effects of Data Augmentation
Polina Kirichenko, Mark Ibrahim, Randall Balestriero +4
Data augmentation (DA) encodes invariance and provides implicit regularization critical to a model's performance in image classification tasks. However, while DA improves average a…
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood, Julius von Kügelgen, Linus Ericsson +4
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, with downstream tasks generally unknown…
Discovering environments with XRM
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim +3
Environment annotations are essential for the success of many out-of-distribution (OOD) generalization methods. Unfortunately, these are costly to obtain and often limited by human…
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…
Birth of a Transformer: A Memory Viewpoint
Alberto Bietti, Vivien Cabannes, Diane Bouchacourt +2
Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their i…
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…