无需反向传播的持续测试时自适应，提升效率与性能
Subspace Optimization for Backpropagation-Free Continual Test-Time Adaptation
- 直接优化归一化层仿射参数，在低维子空间中进行
- 在多个基准上达到最优准确率，运行时间减少50%以上
- 适合需要高效持续适应的部署场景
我们提出PACE，一种无需反向传播的持续测试时自适应系统，直接优化归一化层的仿射参数。现有无导数方法在运行效率与学习能力间难以平衡，或仅限于输入提示更新，或需持续资源密集型适应，无论领域是否稳定。为解决此问题，PACE采用带Fastfood投影的协方差矩阵自适应进化策略，在低维子空间中优化高维仿射参数，实现更优适应性能。此外，通过引入适应停止准则和领域专用向量库，进一步提升运行效率，消除冗余计算。该框架在持续分布偏移下多个基准上达到最先进准确率，相比现有无反向传播方法运行时间减少超过50%。
原文摘要 · Abstract (English)
We introduce PACE, a backpropagation-free continual test-time adaptation system that directly optimizes the affine parameters of normalization layers. Existing derivative-free approaches struggle to balance runtime efficiency with learning capacity, as they either restrict updates to input prompts or require continuous, resource-intensive adaptation regardless of domain stability. To address these limitations, PACE leverages the Covariance Matrix Adaptation Evolution Strategy with the Fastfood projection to optimize high-dimensional affine parameters within a low-dimensional subspace, leading to superior adaptive performance. Furthermore, we enhance the runtime efficiency by incorporating an adaptation stopping criterion and a domain-specialized vector bank to eliminate redundant computation. Our framework achieves state-of-the-art accuracy across multiple benchmarks under continual distribution shifts, reducing runtime by over 50% compared to existing backpropagation-free methods.
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