揭示织锦：持续学习中泛化与遗忘的相互作用
Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning
AI总结：
该研究揭示持续学习中泛化与遗忘的正向相互作用，提出形状-纹理一致性正则化（STCR）技术，可无缝集成于现有持续学习方法，显著提升性能。
摘要：
在人工智能领域，泛化指的是模型在给定任务相关的分布外数据上（而非仅在其训练过的数据上）表现良好的能力。一个AI智能体要表现出色，还必须具备持续学习能力，即智能体能够增量式学习执行一系列任务，且不会遗忘此前习得的用于解决旧任务的知识。直观来看，单个任务内的泛化能让模型学习到可直接应用于新任务的底层特征，从而在持续学习框架中加快后续任务的学习速度并提升性能。反之，持续学习方法通常包含缓解灾难性遗忘的机制，确保早期任务的知识得以保留。这种跨任务的知识保留对提升当前正在进行任务的泛化能力具有作用。尽管这两种能力的相互作用在直觉上颇具合理性，但现有关于持续学习与泛化的文献一直是分别展开研究的。为推动衔接这两个领域的研究，我们首先给出了实证证据，表明这两个领域的能力对彼此存在正向促进作用。接着，基于这一发现，我们提出了一种简单有效的技术——形状-纹理一致性正则化（Shape-Texture Consistency Regularization, STCR），该技术适用于持续学习场景。STCR为每个任务同时学习形状和纹理表征，从而增强泛化能力并缓解遗忘。值得注意的是，大量实验验证了我们的STCR可以无缝集成到现有的持续学习方法中，包括无回放（replay-free）方法。其性能远超单独使用这些持续学习方法，或这些方法与成熟泛化技术结合使用时的性能。
英文摘要：
In AI, generalization refers to a model's ability to perform well on out-of-distribution data related to the given task, beyond the data it was trained on. For an AI agent to excel, it must also possess the continual learning capability, whereby an agent incrementally learns to perform a sequence of tasks without forgetting the previously acquired knowledge to solve the old tasks. Intuitively, generalization within a task allows the model to learn underlying features that can readily be applied to novel tasks, facilitating quicker learning and enhanced performance in subsequent tasks within a continual learning framework. Conversely, continual learning methods often include mechanisms to mitigate catastrophic forgetting, ensuring that knowledge from earlier tasks is retained. This preservation of knowledge over tasks plays a role in enhancing generalization for the ongoing task at hand. Despite the intuitive appeal of the interplay of both abilities, existing literature on continual learning and generalization has proceeded separately. In the preliminary effort to promote studies that bridge both fields, we first present empirical evidence showing that each of these fields has a mutually positive effect on the other. Next, building upon this finding, we introduce a simple and effective technique known as Shape-Texture Consistency Regularization (STCR), which caters to continual learning. STCR learns both shape and texture representations for each task, consequently enhancing generalization and thereby mitigating forgetting. Remarkably, extensive experiments validate that our STCR, can be seamlessly integrated with existing continual learning methods, including replay-free approaches. Its performance surpasses these continual learning methods in isolation or when combined with established generalization techniques by a large margin.
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。
