  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.
