You Only Need a Good Embeddings Extractor to Fix Spurious Correlations

• URL: http://arxiv.org/abs/2212.06254v1
• Date: Mon, 12 Dec 2022 21:42:33 GMT
• Title: You Only Need a Good Embeddings Extractor to Fix Spurious Correlations
• Authors: Raghav Mehta, V\'itor Albiero, Li Chen, Ivan Evtimov, Tamar Glaser, Zhiheng Li, Tal Hassner
• Abstract summary: GroupDRO requires training a model in an end-to-end manner with subgroup labels. We show that we can achieve up to 90% accuracy without using any sub-group information in the training set.
• Score: 26.23962870932271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spurious correlations in training data often lead to robustness issues since models learn to use them as shortcuts. For example, when predicting whether an object is a cow, a model might learn to rely on its green background, so it would do poorly on a cow on a sandy background. A standard dataset for measuring state-of-the-art on methods mitigating this problem is Waterbirds. The best method (Group Distributionally Robust Optimization - GroupDRO) currently achieves 89\% worst group accuracy and standard training from scratch on raw images only gets 72\%. GroupDRO requires training a model in an end-to-end manner with subgroup labels. In this paper, we show that we can achieve up to 90\% accuracy without using any sub-group information in the training set by simply using embeddings from a large pre-trained vision model extractor and training a linear classifier on top of it. With experiments on a wide range of pre-trained models and pre-training datasets, we show that the capacity of the pre-training model and the size of the pre-training dataset matters. Our experiments reveal that high capacity vision transformers perform better compared to high capacity convolutional neural networks, and larger pre-training dataset leads to better worst-group accuracy on the spurious correlation dataset.

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