  Data augmentations are important ingredients in the recipe for training
robust neural networks, especially in computer vision. A fundamental question
is whether neural network features encode data augmentation transformations. To
answer this question, we introduce a systematic approach to investigate which
layers of neural networks are the most predictive of augmentation
transformations. Our approach uses features in pre-trained vision models with
minimal additional processing to predict common properties transformed by
augmentation (scale, aspect ratio, hue, saturation, contrast, and brightness).
Surprisingly, neural network features not only predict data augmentation
transformations, but they predict many transformations with high accuracy.
After validating that neural networks encode features corresponding to
augmentation transformations, we show that these features are encoded in the
early layers of modern CNNs, though the augmentation signal fades in deeper
layers.
