An Investigation of Why Overparameterization Exacerbates Spurious Correlations

Jul 12, 2020



Increasing model capacity well beyond the point of zero training error has been observed to improve average test accuracy. However, such overparameterized models have been recently shown to obtain low worst-group accuracy — i.e., low accuracy on atypical groups of test examples — when there are spurious correlations that hold for the majority of training examples. We show on two image datasets that in contrast to average accuracy, overparameterization hurts worst-group accuracy in the presence of spurious correlations. We replicate this surprising phenomenon in a synthetic example and identify properties of the data distribution that induce the detrimental effect of overparameterization on worst-group accuracy. Our analysis leads us to show that a counter-intuitive approach of subsampling the majority group yields high worst-group accuracy in the overparameterized regime, whereas upweighting the minority does not. Our results suggest that when it comes to achieving high worst-group accuracy, there is a tension between using overparameterized models vs. using all of the training data.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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