  Deep neural networks have useful applications in many different tasks,
however their performance can be severely affected by changes in the data
distribution. For example, in the biomedical field, their performance can be
affected by changes in the data (different machines, populations) between
training and test datasets. To ensure robustness and generalization to
real-world scenarios, test-time adaptation has been recently studied as an
approach to adjust models to a new data distribution during inference.
Test-time batch normalization is a simple and popular method that achieved
compelling performance on domain shift benchmarks. It is implemented by
recalculating batch normalization statistics on test batches. Prior work has
focused on analysis with test data that has the same label distribution as the
training data. However, in many practical applications this technique is
vulnerable to label distribution shifts, sometimes producing catastrophic
failure. This presents a risk in applying test time adaptation methods in
deployment. We propose to tackle this challenge by only selectively adapting
channels in a deep network, minimizing drastic adaptation that is sensitive to
label shifts. Our selection scheme is based on two principles that we
empirically motivate: (1) later layers of networks are more sensitive to label
shift (2) individual features can be sensitive to specific classes. We apply
the proposed technique to three classification tasks, including CIFAR10-C,
Imagenet-C, and diagnosis of fatty liver, where we explore both covariate and
label distribution shifts. We find that our method allows to bring the benefits
of TTA while significantly reducing the risk of failure common in other
methods, while being robust to choice in hyperparameters.
