Frustratingly Simple Domain Generalization via Image Stylization
This addresses the issue of CNNs failing to generalize to new domains, which is critical for real-world applications, though it is incremental as it builds on existing stylization approaches.
The paper tackles the Domain Generalization problem by augmenting datasets with stylized images to correct CNN biases, achieving results surpassing or comparable to state-of-the-art methods.
Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i.i.d. from a given domain. However, CNNs do not readily generalize to new domains with different statistics, a setting that is simple for humans. In this work, we address the Domain Generalization problem, where the classifier must generalize to an unknown target domain. Inspired by recent works that have shown a difference in biases between CNNs and humans, we demonstrate an extremely simple yet effective method, namely correcting this bias by augmenting the dataset with stylized images. In contrast with existing stylization works, which use external data sources such as art, we further introduce a method that is entirely in-domain using no such extra sources of data. We provide a detailed analysis as to the mechanism by which the method works, verifying our claim that it changes the shape/texture bias, and demonstrate results surpassing or comparable to the state of the arts that utilize much more complex methods.