Poster: Time-Efficient Sparse and Lightweight Adaptation for Real-Time Mobile Application

Abstract

When deployed in mobile scenarios, deep learning models often suffer from performance degradation due to domain shifts. Test-Time Adaptation (TTA) offers a viable solution, but current approaches face latency issues on resource-constrained mobile devices. We propose TESLA: Time-Efficient Sparse and Lightweight Adaptation strategy for real-time mobile applications, which skips adaptation for specific batches to increase the inference sample rate. Our method balances model accuracy and inference speed by accumulating domain-informative samples from non-adapted batches and sparsely adapting them. Experiments on edge devices demonstrate competitive accuracy even with sparse adaptation rates, highlighting the effectiveness of our approach in real-time mobile applications. Our strategy can seamlessly integrate with existing lightweight adaptation and optimization algorithms, further accelerating inference across diverse mobile systems.