Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers

1 code implementation • 24 Jun 2021 • Katelyn Morrison, Benjamin Gilby, Colton Lipchak, Adam Mattioli, Adriana Kovashka

We find that vision transformer architectures are inherently more robust to corruptions than the ResNet-50 and MLP-Mixers.

Data Augmentation