Appendix A Patch based Negative Data Augmentation Reduces Texture Bias

Figure 5: ViTs trained only on our patch-based transformations exhibit stronger texture bias. Each bar is the texture accuracy ( %) on Conflict Stimuli (Geirhos et al., 2018), and a higher texture accuracy indicates the model has a higher bias towards texture. The "texture accuracy" is defined as the percentage of images that are classified as the "texture" label, provided the image is classified as either "texture" or "shape" label. The baseline model is ViT -B/16 in (Dosovitskiy et al., 2021) trained on original images. Other models are trained on patch-based transformed images, e.g., "P-Shuffle" stands for a ViT -B/16 model trained on patch-based shuffled images.