增强可塑性以用于首会话适应的持续学习
Enhancing Plasticity for First Session Adaptation Continual Learning
AI总结：
针对类增量学习中首会话适应方法在异质任务分布下失效的问题，提出PLASTIC方法，通过测试时适应动态微调LayerNorm参数并引入教师-学生蒸馏框架，在保持稳定性的同时恢复可塑性，实验表明其优于现有方法。
摘要：
将大型预训练模型（PTMs）集成到类增量学习（CIL）中，促进了计算高效策略的发展，如首会话适应（FSA），该策略仅在第一任务上微调模型，并在后续任务中保持模型冻结。尽管在同类任务序列中有效，但这些方法在面对现实世界任务分布的异质性时表现不佳。我们提出了类增量学习中的可塑性增强测试时适应（PLASTIC）方法，该方法在CIL中恢复可塑性，同时保持模型稳定性。PLASTIC利用测试时适应（TTA），通过在未标记的测试数据上动态微调LayerNorm参数，使模型能够适应不断变化的任务，并提高对数据损坏的鲁棒性。为了防止TTA引起的模型发散并在任务间保持稳定学习，我们引入了一个教师-学生蒸馏框架，确保适应过程受控且具有泛化性。在多个基准上的大量实验表明，PLASTIC始终优于传统和最先进的基于PTM的CIL方法，同时表现出对数据损坏的内在鲁棒性。代码可在 https://github.com/IemProg/PLASTIC 获取。
英文摘要：
The integration of large pre-trained models (PTMs) into Class-Incremental Learning (CIL) has facilitated the development of computationally efficient strategies such as First-Session Adaptation (FSA), which fine-tunes the model solely on the first task while keeping it frozen for subsequent tasks. Although effective in homogeneous task sequences, these approaches struggle when faced with the heterogeneity of real-world task distributions. We introduce Plasticity-Enhanced Test-Time Adaptation in Class-Incremental Learning (PLASTIC), a method that reinstates plasticity in CIL while preserving model stability. PLASTIC leverages Test-Time Adaptation (TTA) by dynamically fine-tuning LayerNorm parameters on unlabeled test data, enabling adaptability to evolving tasks and improving robustness against data corruption. To prevent TTA-induced model divergence and maintain stable learning across tasks, we introduce a teacher-student distillation framework, ensuring that adaptation remains controlled and generalizable. Extensive experiments across multiple benchmarks demonstrate that PLASTIC consistently outperforms both conventional and state-of-the-art PTM-based CIL approaches, while also exhibiting inherent robustness to data corruptions. Code is available at: https://github.com/IemProg/PLASTIC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
