When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation

Understand

Data Augmentation (DA) is known to improve the generalizability of deep neural networks.

• Most existing DA techniques naively add a certain number of augmented samples without considering the quality and the added computational cost of these samples.
• To tackle this problem, a common strategy, adopted by several state-of-the-art DA methods, is to adaptively generate or re-weight augmented samples with respect to the task objective during training.
• However, these adaptive DA methods: (1) are computationally expensive and not sample-efficient, and (2) are designed merely for a specific setting.