Effective Restoration of Source Knowledge in Continual Test Time
  Adaptation
Fahim Faisal Niloy Sk Miraj Ahmed Dripta S. Raychaudhuri Samet Oymak Amit K. Roy-Chowdhury
Traditional test-time adaptation (TTA) methods face significant challenges in
adapting to dynamic environments characterized by continuously changing
long-term target distributions. These challenges primarily stem from two
factors: catastrophic forgetting of previously learned valuable source
knowledge and gradual error accumulation caused by miscalibrated pseudo labels.
To address these issues, this paper introduces an unsupervised domain change
detection method that is capable of identifying domain shifts in dynamic
environments and subsequently resets the model parameters to the original
source pre-trained values. By restoring the knowledge from the source, it
effectively corrects the negative consequences arising from the gradual
deterioration of model parameters caused by ongoing shifts in the domain. Our
method involves progressive estimation of global batch-norm statistics specific
to each domain, while keeping track of changes in the statistics triggered by
domain shifts. Importantly, our method is agnostic to the specific adaptation
technique employed and thus, can be incorporated to existing TTA methods to
enhance their performance in dynamic environments. We perform extensive
experiments on benchmark datasets to demonstrate the superior performance of
our method compared to state-of-the-art adaptation methods.