Distributionally Robust Classification on a Data Budget
Benjamin Feuer Ameya Joshi Minh Pham Chinmay Hegde
Real world uses of deep learning require predictable model behavior under
distribution shifts. Models such as CLIP show emergent natural distributional
robustness comparable to humans, but may require hundreds of millions of
training samples. Can we train robust learners in a domain where data is
limited? To rigorously address this question, we introduce JANuS (Joint
Annotations and Names Set), a collection of four new training datasets with
images, labels, and corresponding captions, and perform a series of carefully
controlled investigations of factors contributing to robustness in image
classification, then compare those results to findings derived from a
large-scale meta-analysis. Using this approach, we show that standard ResNet-50
trained with the cross-entropy loss on 2.4 million image samples can attain
comparable robustness to a CLIP ResNet-50 trained on 400 million samples. To
our knowledge, this is the first result showing (near) state-of-the-art
distributional robustness on limited data budgets. Our dataset is available at
\url{https://huggingface.co/datasets/penfever/JANuS_dataset}, and the code used
to reproduce our experiments can be found at
\url{https://github.com/penfever/vlhub/}.