Mitigating Simplicity Bias in Deep Learning for Improved OOD
  Generalization and Robustness
Bhavya Vasudeva Kameron Shahabi Vatsal Sharan
Neural networks (NNs) are known to exhibit simplicity bias where they tend to
prefer learning 'simple' features over more 'complex' ones, even when the
latter may be more informative. Simplicity bias can lead to the model making
biased predictions which have poor out-of-distribution (OOD) generalization. To
address this, we propose a framework that encourages the model to use a more
diverse set of features to make predictions. We first train a simple model, and
then regularize the conditional mutual information with respect to it to obtain
the final model. We demonstrate the effectiveness of this framework in various
problem settings and real-world applications, showing that it effectively
addresses simplicity bias and leads to more features being used, enhances OOD
generalization, and improves subgroup robustness and fairness. We complement
these results with theoretical analyses of the effect of the regularization and
its OOD generalization properties.