SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
Taesik Gong Yewon Kim Taeckyung Lee Sorn Chottananurak Sung-Ju Lee
Test-time adaptation (TTA) aims to address distributional shifts between
training and testing data using only unlabeled test data streams for continual
model adaptation. However, most TTA methods assume benign test streams, while
test samples could be unexpectedly diverse in the wild. For instance, an unseen
object or noise could appear in autonomous driving. This leads to a new threat
to existing TTA algorithms; we found that prior TTA algorithms suffer from
those noisy test samples as they blindly adapt to incoming samples. To address
this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel
TTA algorithm that is robust to noisy samples. The key enabler of SoTTA is
two-fold: (i) input-wise robustness via high-confidence uniform-class sampling
that effectively filters out the impact of noisy samples and (ii)
parameter-wise robustness via entropy-sharpness minimization that improves the
robustness of model parameters against large gradients from noisy samples. Our
evaluation with standard TTA benchmarks with various noisy scenarios shows that
our method outperforms state-of-the-art TTA methods under the presence of noisy
samples and achieves comparable accuracy to those methods without noisy
samples. The source code is available at https://github.com/taeckyung/SoTTA .