持续学习的贝叶斯方法：综述
The Bayesian Approach to Continual Learning: An Overview
AI总结：
探讨持续学习问题，利用贝叶斯推理方法，避免遗忘旧知识，无需从头重训，总结其不同设置、算法分类、现状及与其他领域联系，分析挑战并展望未来研究方向。
摘要：
持续学习是一种在线范式，学习者需不断积累知识且不忘过去经验。它与贝叶斯推理契合。本文综述贝叶斯持续学习的不同设置，讨论其定义、与相关领域联系，介绍算法分类，分析现状，探讨与发展心理学的关联，阐述挑战并指出未来研究方向。
英文摘要：
Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps. Importantly, the learner is required to extend and update its knowledge without forgetting about the learning experience acquired from the past, and while avoiding the need to retrain from scratch. Given its sequential nature and its resemblance to the way humans think, continual learning offers an opportunity to address several challenges which currently stand in the way of widening the range of applicability of deep models to further real-world problems. The continual need to update the learner with data arriving sequentially strikes inherent congruence between continual learning and Bayesian inference which provides a principal platform to keep updating the prior beliefs of a model given new data, without completely forgetting the knowledge acquired from the old data. This survey inspects different settings of Bayesian continual learning, namely task-incremental learning and class-incremental learning. We begin by discussing definitions of continual learning along with its Bayesian setting, as well as the links with related fields, such as domain adaptation, transfer learning and meta-learning. Afterwards, we introduce a taxonomy offering a comprehensive categorization of algorithms belonging to the Bayesian continual learning paradigm. Meanwhile, we analyze the state-of-the-art while zooming in on some of the most prominent Bayesian continual learning algorithms to date. Furthermore, we shed some light on links between continual learning and developmental psychology, and correspondingly introduce analogies between both fields. We follow that with a discussion of current challenges, and finally conclude with potential areas for future research on Bayesian continual learning.
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。
