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Our approach is also efficient, doing so without any fm finetuning and only a fixed set of frozen fm embeddings. Stanford university researchers developed contrastive adapters, an efficient method to enhance the group robustness of frozen foundation models without full finetuning. Unfortunately, existing methods to improve robustness require retraining, which can be prohibitively expensive on large foundation models.

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We provide sample scripts below While prior work only trains adapters with class labels, we add a contrastive objective to explicitly learn similar embeddings for initially dissimilar fm embeddings. We support the following choices:

We thus propose a first adapter training method designed to improve fm robustness to group shifts