Online Subloop Search via Uncertainty Quantization for Efficient Test-Time Adaptation

Online test-time adaptation (OTTA) methods have demonstrated their effectiveness in real-time adapting to the target domain for speech recognition tasks. However, a common thread among these existing methods is their reliance on repetitive learning for each test utterance through a subloop, imposing prohibitive computational costs. This paper highlights the inefficiency inherent in applying a uniform number of subloop iterations to every test sample. To address this issue, we propose the online subloop search (OSS) method, which implicitly adjusts the number of iterations based on the test sample and domain characteristics. The proposed method operates within a framework comprising a chaser model updated via stochastic gradient descent and a leader model updated through the exponential moving average. The OSS method quantifies and quantizes the uncertainty in the chaser model relative to the leader model, using the quantized value to predict the number of iterations for the subloop.