Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., adding sunglasses or changing backgrounds.
translating training samples along many semantic directions in the feature space can effectively augment the dataset to improve generalization.