Regularizing Deep Networks with Semantic Data Augmentation

Understand

Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks.

• Conventional data augmentation schemes, e.g., flipping, translation or rotation, are low-level, data-independent and class-agnostic operations, leading to limited diversity for augmented samples.
• To this end, we propose a novel semantic data augmentation algorithm to complement traditional approaches.
• The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i.e., certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., changing the background or view angle of an object.