Mitigating Missing Feature Channels at Inference Stage: Test-Time Adaptation Through Self-Training With Data Imputation

The robustness of deep learning model can be compromised by out-of-distribution (OOD) testing data. Test-time adaptation (TTA) emerges as an efficient method to mitigate the distribution gap by tuning model weights at inference stage. TTA are mainly demonstrated on robustifying model on additive visual corruptions or adversarial attacks. In this work, we specify an overlooked type of OOD where feature channels could be missing in testing data, potentially due to sensor fault. We reveal that self-training and data imputation can improve the model’s generalization to data with missing feature channel. To address the uncertainty associated with imputed samples, we fuse predictions from imputed and weakly-augmented samples for more reliable pseudo labels. We evaluate the effectiveness on multiple image classification benchmarks with synthesized and realistic missing feature channels, and our proposed method outperforms state-of-the-art TTA methods on all benchmarks.