  Recent powerful vision classifiers are biased towards textures, while shape
information is overlooked by the models. A simple attempt by augmenting
training images using the artistic style transfer method, called Stylized
ImageNet, can reduce the texture bias. However, Stylized ImageNet approach has
two drawbacks in fidelity and diversity. First, the generated images show low
image quality due to the significant semantic gap betweeen natural images and
artistic paintings. Also, Stylized ImageNet training samples are pre-computed
before training, resulting in showing the lack of diversity for each sample. We
propose a StyleAugment by augmenting styles from the mini-batch. StyleAugment
does not rely on the pre-defined style references, but generates augmented
images on-the-fly by natural images in the mini-batch for the references.
Hence, StyleAugment let the model observe abundant confounding cues for each
image by on-the-fly the augmentation strategy, while the augmented images are
more realistic than artistic style transferred images. We validate the
effectiveness of StyleAugment in the ImageNet dataset with robustness
benchmarks, such as texture de-biased accuracy, corruption robustness, natural
adversarial samples, and occlusion robustness. StyleAugment shows better
generalization performances than previous unsupervised de-biasing methods and
state-of-the-art data augmentation methods in our experiments.
