Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios
Shuang Li Longhui Yuan Binhui Xie Tao Yang
Test-time adaptation (TTA) adapts the pre-trained models to test
distributions during the inference phase exclusively employing unlabeled test
data streams, which holds great value for the deployment of models in
real-world applications. Numerous studies have achieved promising performance
on simplistic test streams, characterized by independently and uniformly
sampled test data originating from a fixed target data distribution. However,
these methods frequently prove ineffective in practical scenarios, where both
continual covariate shift and continual label shift occur simultaneously, i.e.,
data and label distributions change concurrently and continually over time. In
this study, a more challenging Practical Test-Time Adaptation (PTTA) setup is
introduced, which takes into account the concurrent presence of continual
covariate shift and continual label shift, and we propose a Generalized Robust
Test-Time Adaptation (GRoTTA) method to effectively address the difficult
problem. We start by steadily adapting the model through Robust Parameter
Adaptation to make balanced predictions for test samples. To be specific,
firstly, the effects of continual label shift are eliminated by enforcing the
model to learn from a uniform label distribution and introducing recalibration
of batch normalization to ensure stability. Secondly, the continual covariate
shift is alleviated by employing a source knowledge regularization with the
teacher-student model to update parameters. Considering the potential
information in the test stream, we further refine the balanced predictions by
Bias-Guided Output Adaptation, which exploits latent structure in the feature
space and is adaptive to the imbalanced label distribution. Extensive
experiments demonstrate GRoTTA outperforms the existing competitors by a large
margin under PTTA setting, rendering it highly conducive for adoption in
real-world applications.