Abstract(s)
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 by
recalculating batch normalization statistics on test batches. However, in many
practical applications this technique is vulnerable to label distribution shifts. We
propose to tackle this challenge by only selectively adapting channels in a deep
network, minimizing drastic adaptation that is sensitive to label shifts. We find that
adapted models significantly improve the performance compared to the baseline
models and counteract unknown label shifts.