Appendices

The supplementary material is organized as follows. We first discuss additional related work and provide experiment details in Section 2 and Appendix B respectively. In Appendix C, we provide additional experiments to further validate the extreme nature of Simplicity Bias (SB). Then, in Appendix D, we provide additional information about the experiment setup used to to show that extreme SB can hurt generalization. We evaluate the extent to which ensemble methods and adversarial training mitigate Simplicity Bias (SB) in Appendix E. Finally, we provide the proof of Theorem 1 in Appendix F. In this section, we provide a more thorough discussion of relevant work related to margin-based generalization bounds, adversarial attacks and robustness, and out-of-distribution (OOD) examples. Margin-based generalization bounds: Building up on the classical work of [3], recent works try to obtain tighter generalization bounds for neural networks in terms of normalized margin [4, 50, 18, 22]. Here, margin is defined as the difference in the probability of the true label and the largest probability of the incorrect labels.