Learning Loss for Test-Time Augmentation

Understand

Data augmentation has been actively studied for robust neural networks.

• Most of the recent data augmentation methods focus on augmenting datasets during the training phase.
• At the testing phase, simple transformations are still widely used for test-time augmentation.
• This paper proposes a novel instance-level test-time augmentation that efficiently selects suitable transformations for a test input.