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Workshop: The First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward

On the Connection between Pre-training Data Diversity and Robustness

Vivek Ramanujan · Vivek Ramanujan · Thao Nguyen · Thao Nguyen · Ludwig Schmidt · Ali Farhadi · Ali Farhadi


Our work studies the implications of transfer learning on model behavior beyond accuracy: how does the pre-training distribution affect the downstream robustness of a fine-tuned model? We analyze model robustness using the framework proposed by Taori et al. (2020), which demonstrates that in-distribution and out-of-distribution performances are highly correlated along a robustness linear trend. We explore various interventions that significantly alter the pre-training distribution, including label space, label semantics, and the pre-training dataset itself. In most cases, changes during pre-training have minimal impact on the original linear trend produced by models pre-trained on the full ImageNet dataset. We demonstrate these findings on pre-training distributions constructed from ImageNet and iNaturalist, and fine-tuning data from the iWildCams-WILDS benchmark.

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