On-demand Test-time Adaptation for Edge Devices
This work addresses the challenge of making test-time adaptation practical for resource-constrained edge devices, offering an incremental improvement over existing CTTA methods.
The paper tackles the problem of high memory and energy overhead in continual test-time adaptation (CTTA) for edge devices by introducing an on-demand TTA paradigm that triggers adaptation only when significant domain shifts are detected, achieving comparable or better performance while drastically reducing computation and energy costs.
Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data. While achieving optimal accuracy, existing CTTA approaches present poor real-world applicability on resource-constrained edge devices, due to the substantial memory overhead and energy consumption. In this work, we first introduce a novel paradigm -- on-demand TTA -- which triggers adaptation only when a significant domain shift is detected. Then, we present OD-TTA, an on-demand TTA framework for accurate and efficient adaptation on edge devices. OD-TTA comprises three innovative techniques: 1) a lightweight domain shift detection mechanism to activate TTA only when it is needed, drastically reducing the overall computation overhead, 2) a source domain selection module that chooses an appropriate source model for adaptation, ensuring high and robust accuracy, 3) a decoupled Batch Normalization (BN) update scheme to enable memory-efficient adaptation with small batch sizes. Extensive experiments show that OD-TTA achieves comparable and even better performance while reducing the energy and computation overhead remarkably, making TTA a practical reality.