@inproceedings{
chen2024crossdevice,
title={Cross-Device Collaborative Test-Time Adaptation},
author={Guohao Chen and Shuaicheng Niu and Deyu Chen and Shuhai Zhang and Changsheng Li and Yuanqing Li and Mingkui Tan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=YyMiO0DWmI}
}
In this paper, we propose test-time Collaborative Lifelong Adaptation (CoLA), which is a general paradigm that can be incorporated with existing advanced TTA methods to boost the adaptation performance and efficiency in a multi-device collaborative manner. Specifically, we maintain and store a set of device-shared _domain knowledge vectors_, which accumulates the knowledge learned from all devices during their lifelong adaptation process. Based on this, CoLA conducts two collaboration strategies for devices with different computational resources and latency demands. 1) Knowledge reprogramming learning strategy jointly learns new domain-specific model parameters and a reweighting term to reprogram existing shared domain knowledge vectors, termed adaptation on _principal agents_. 2) Similarity-based knowledge aggregation strategy solely aggregates the knowledge stored in shared domain vectors according to domain similarities in an optimization-free manner, termed adaptation on _follower agents_. Experiments verify that CoLA is simple but effective, which boosts the efficiency of TTA and demonstrates remarkable superiority in collaborative, lifelong, and single-domain TTA scenarios, e.g., on follower agents, we enhance accuracy by over 30\% on ImageNet-C while maintaining nearly the same efficiency as standard inference. The source code is available at https://github.com/Cascol-Chen/COLA.
Cross-Device Collaborative Test-Time Adaptation
Guohao Chen Shuaicheng Niu Deyu Chen Shuhai Zhang Changsheng Li Yuanqing Li Mingkui Tan