  A common strategy for improving model robustness is through data
augmentations. Data augmentations encourage models to learn desired
invariances, such as invariance to horizontal flipping or small changes in
color. Recent work has shown that arbitrary style transfer can be used as a
form of data augmentation to encourage invariance to textures by creating
painting-like images from photographs. However, a stylized photograph is not
quite the same as an artist-created painting. Artists depict perceptually
meaningful cues in paintings so that humans can recognize salient components in
scenes, an emphasis which is not enforced in style transfer. Therefore, we
study how style transfer and paintings differ in their impact on model
robustness. First, we investigate the role of paintings as style images for
stylization-based data augmentation. We find that style transfer functions well
even without paintings as style images. Second, we show that learning from
paintings as a form of perceptual data augmentation can improve model
robustness. Finally, we investigate the invariances learned from stylization
and from paintings, and show that models learn different invariances from these
differing forms of data. Our results provide insights into how stylization
improves model robustness, and provide evidence that artist-created paintings
can be a valuable source of data for model robustness.
