To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance degradation of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative methods address continual learning, and how they are adapted to particular challenges in realistic applications. Through an in-depth discussion of promising directions, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.

翻译：为应对现实世界的动态变化，智能系统需要在其生命周期中逐步获取、更新、积累并利用知识。这种被称为持续学习的能力，为人工智能系统实现自适应发展提供了基础。一般而言，持续学习受到灾难性遗忘的显著限制——学习新任务通常会导致旧任务性能急剧下降。近年来，大量研究进展极大地拓展了人们对持续学习的理解及其应用场景。该方向日益增长且广泛的关注度，既彰显了其现实意义，也体现了其复杂程度。本文对持续学习进行了全面综述，旨在衔接基础设定、理论基础、代表性方法与实际应用。基于现有理论与实证结果，我们总结出持续学习的总体目标为：在资源效率约束下，确保适当的稳定性-可塑性权衡以及充分的任务内/任务间泛化能力。随后，我们提出了一种前沿且精细的分类体系，深入分析了代表性方法如何解决持续学习问题，以及它们如何适配实际应用中的特定挑战。通过对未来方向的深度探讨，我们相信这种整体视角将为该领域及更广泛的研究提供重要助力。