  Machine Learning models are prone to fail when test data are different from
training data, a situation often encountered in real applications known as
distribution shift. While still valid, the training-time knowledge becomes less
effective, requiring a test-time adaptation to maintain high performance.
Following approaches that assume batch-norm layer and use their statistics for
adaptation, we propose a Test-Time Adaptation with Principal Component Analysis
(TTAwPCA), which presumes a fitted PCA and adapts at test time a spectral
filter based on the singular values of the PCA for robustness to corruptions.
TTAwPCA combines three components: the output of a given layer is decomposed
using a Principal Component Analysis (PCA), filtered by a penalization of its
singular values, and reconstructed with the PCA inverse transform. This generic
enhancement adds fewer parameters than current methods. Experiments on
CIFAR-10-C and CIFAR- 100-C demonstrate the effectiveness and limits of our
method using a unique filter of 2000 parameters.
