Dynamic Test-Time Augmentation via Differentiable Functions

Understand

Distribution shifts, which often occur in the real world, degrade the accuracy of deep learning systems, and thus improving robustness to distribution shifts is essential for practical applications.

• To improve robustness, we study an image enhancement method that generates recognition-friendly images without retraining the recognition model.
• We propose a novel image enhancement method, DynTTA, which is based on differentiable data augmentation techniques and generates a blended image from many augmented images to improve the recognition accuracy under distribution shifts.
• In addition to standard data augmentations, DynTTA also incorporates deep neural network-based image transformation, further improving the robustness.