@inproceedings{
vianna2024channel,
title={Channel Selection for Test-Time Adaptation Under Distribution Shift},
author={Pedro Vianna and Muawiz Sajjad Chaudhary and An Tang and Guy Cloutier and Guy Wolf and Michael Eickenberg and Eugene Belilovsky},
booktitle={NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models},
year={2024},
url={https://openreview.net/forum?id=BTOBu7y2ZD}
}
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.
Channel Selection for Test-Time Adaptation Under Distribution Shift
Pedro Vianna Muawiz Sajjad Chaudhary An Tang Guy Cloutier Guy Wolf Michael Eickenberg Eugene Belilovsky