VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

TL;DR

VITA introduces a novel augmentation method combining tangent transfer and multi-source vicinal samples to generate on-manifold data, significantly enhancing out-of-distribution robustness in computer vision models.

Contribution

The paper proposes VITA, a multi-source vicinal transfer augmentation technique that improves robustness by generating on-manifold samples using tangent transfer and generative models.

Findings

VITA outperforms existing augmentation methods on corruption benchmarks.

VITA effectively generates diverse on-manifold samples.

The method enhances model robustness against various image corruptions.

Abstract

Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptions. However, the samples produced by popular augmentation strategies deviate significantly from the underlying data manifold. As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. The proposed VITA consists of two complementary parts: tangent transfer and integration of multi-source vicinal samples. The tangent transfer creates initial augmented samples for improving corruption robustness. The integration employs a generative model to characterize the underlying manifold built by vicinal samples, facilitating the generation of on-manifold samples. Our proposed VITA significantly outperforms the current state-of-the-art augmentation methods, demonstrated in extensive experiments on corruption benchmarks.