标题： ExPaMoE：一种可扩展的并行专家混合模型用于持续测试时适应 显示英文标题

标题： ExPaMoE: An Expandable Parallel Mixture of Experts for Continual Test-Time Adaptation

摘要： 持续测试时适应（CTTA）旨在使模型能够在不断变化的分布偏移下，实时适应未标记数据流。 然而，现有的CTTA方法通常依赖于所有领域共享的模型参数，这使得它们在面对大规模或非平稳领域偏移时容易受到特征纠缠和灾难性遗忘的影响。 为了解决这一限制，我们提出了\textbf{实验模型}，一个基于\emph{可扩展的并行专家混合模型}架构的新框架。 ExPaMoE通过具有令牌引导特征分离的双分支专家设计，解耦领域通用和领域特定知识，并基于\emph{光谱感知在线域鉴别器}（SODD）动态扩展其专家池，该方法使用频域线索实时检测分布变化。 大量实验表明ExPaMoE在各种CTTA场景中表现出优越性。 我们在标准基准上评估了我们的方法，包括CIFAR-10C、CIFAR-100C、ImageNet-C和Cityscapes-to-ACDC用于语义分割。 此外，我们引入了\textbf{ImageNet++}，一个大规模且现实的CTTA基准，该基准由多个ImageNet衍生数据集构建而成，以更好地反映复杂领域演变下的长期适应。 ExPaMoE始终优于先前方法，表现出强大的鲁棒性、可扩展性和抗遗忘能力。 显示英文摘要

摘要： Continual Test-Time Adaptation (CTTA) aims to enable models to adapt on-the-fly to a stream of unlabeled data under evolving distribution shifts. However, existing CTTA methods typically rely on shared model parameters across all domains, making them vulnerable to feature entanglement and catastrophic forgetting in the presence of large or non-stationary domain shifts. To address this limitation, we propose \textbf{ExPaMoE}, a novel framework based on an \emph{Expandable Parallel Mixture-of-Experts} architecture. ExPaMoE decouples domain-general and domain-specific knowledge via a dual-branch expert design with token-guided feature separation, and dynamically expands its expert pool based on a \emph{Spectral-Aware Online Domain Discriminator} (SODD) that detects distribution changes in real-time using frequency-domain cues. Extensive experiments demonstrate the superiority of ExPaMoE across diverse CTTA scenarios. We evaluate our method on standard benchmarks including CIFAR-10C, CIFAR-100C, ImageNet-C, and Cityscapes-to-ACDC for semantic segmentation. Additionally, we introduce \textbf{ImageNet++}, a large-scale and realistic CTTA benchmark built from multiple ImageNet-derived datasets, to better reflect long-term adaptation under complex domain evolution. ExPaMoE consistently outperforms prior arts, showing strong robustness, scalability, and resistance to forgetting.