提出自适应重初始化策略，提升模型长期持续域适应能力。
Adaptive and Balanced Re-initialization for Long-timescale Continual Test-time Domain Adaptation
- 根据标签翻转变化动态调整重初始化间隔
- 在多个基准上实现优于现有方法的长期性能
- 适合需要长期稳定部署的实时域适应场景
持续测试时域适应（CTTA）旨在使模型在非平稳环境中随时间保持良好性能。尽管先前方法已优化适应过程，但一个关键问题仍存：模型能否在长时间内持续适应不断变化的环境？本文通过基于重初始化的方法探索长期CTTA的改进。首先观察到长期性能与标签翻转轨迹模式相关，据此提出简单有效的自适应平衡重初始化（ABR）策略。ABR采用基于标签翻转变化的自适应间隔进行权重重初始化。该方法在多个广泛使用的CTTA基准上验证，表现显著优于现有方法。
原文摘要 · Abstract (English)
Continual test-time domain adaptation (CTTA) aims to adjust models so that they can perform well over time across non-stationary environments. While previous methods have made considerable efforts to optimize the adaptation process, a crucial question remains: Can the model adapt to continually changing environments over a long time? In this work, we explore facilitating better CTTA in the long run using a re-initialization (or reset) based method. First, we observe that the long-term performance is associated with the trajectory pattern in label flip. Based on this observed correlation, we propose a simple yet effective policy, Adaptive-and-Balanced Re-initialization (ABR), towards preserving the model's long-term performance. In particular, ABR performs weight re-initialization using adaptive intervals. The adaptive interval is determined based on the change in label flip. The proposed method is validated on extensive CTTA benchmarks, achieving superior performance.
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