Shape-Biased CNNs Are Not Always Superior in Out-of-Distribution Robustness

Abstract

In recent years, Out-of-Distribution (o.o.d) Robustness has garnered increasing attention in Deep Learning, and shape-biased Convolutional Neural Networks (CNNs) are believed to exhibit higher robustness, attributed to the inherent shape-based decision rule of human cognition. In this work, we delve deeper into the intricate relationship between shape/texture information and o.o.d robustness by leveraging a carefully curated "Category-Balanced ImageNet" dataset. We find that shape information is not always superior in distinguishing distinct categories and shape-biased model is not always superior across various o.o.d scenarios. Motivated by these insightful findings, we design a novel method named Shape-Texture Adaptive Recombination (STAR) to achieve higher o.o.d robustness. A category-balanced dataset is firstly used to pretrain a debiased backbone and three specialized heads, each adept at robustly extracting shape, texture, and debiased features. Subsequently, an instance-adaptive recombination head is trained to adaptively adjust the contributions of these distinctive features for each given instance. Through comprehensive experiments, our proposed method achieves state-of-the-art o.o.d robustness across various scenarios such as image corruptions, adversarial attacks, style shifts, and dataset shifts, demonstrating its effectiveness.