To effectively manage the complexities of real-world dynamic environments, continual learning must incrementally acquire, update, and accumulate knowledge from a stream of tasks of different nature without suffering from catastrophic forgetting of prior knowledge. While this capability is innate to human cognition, it remains a significant challenge for modern deep learning systems. At the heart of this challenge lies the stability-plasticity dilemma: the need to balance leveraging prior knowledge, integrating novel information, and allocating model capacity adaptively based on task complexity and synergy. In this paper, we propose a novel exemplar-free class-incremental continual learning (ExfCCL) framework that addresses these issues through a Hierarchical Exploration-Exploitation (HEE) approach. The core of our method is a HEE-guided efficient neural architecture search (HEE-NAS) that enables a learning-to-adapt backbone via four primitive operations - reuse, new, adapt, and skip - thereby serving as an internal memory that dynamically updates selected components across streaming tasks. To address the task ID inference problem in ExfCCL, we exploit an external memory of task centroids proposed in the prior art. We term our method CHEEM (Continual Hierarchical-Exploration-Exploitation Memory). CHEEM is evaluated on the challenging MTIL and VDD benchmarks using both Tiny and Base Vision Transformers and a proposed holistic Figure-of-Merit (FoM) metric. It significantly outperforms state-of-the-art prompting-based continual learning methods, closely approaching full fine-tuning upper bounds. Furthermore, it learns adaptive model structures tailored to individual tasks in a semantically meaningful way. Our code is available at https://github.com/savadikarc/cheem .

翻译：为有效应对真实动态环境的复杂性，持续学习必须能够从不同性质的任务流中逐步获取、更新并积累知识，同时避免灾难性遗忘先前知识。尽管这种能力是人类认知的本能，但对现代深度学习系统而言仍是一项重大挑战。该挑战的核心在于稳定性-可塑性困境：需要平衡利用先验知识、整合新信息，并根据任务复杂度与协同性自适应地分配模型容量。本文提出了一种新颖的无样本类增量持续学习（ExfCCL）框架，通过层次化探索-利用（HEE）方法解决上述问题。该框架核心是一种HEE引导的高效神经网络架构搜索方法（HEE-NAS），它能通过四种基本操作——复用、新增、适配与跳过——实现可学习自适应主干网络，从而作为内部记忆体动态更新跨流式任务的选定组件。针对ExfCCL中的任务ID推断问题，我们利用先前工作中提出的任务质心外部记忆。我们将所提方法命名为CHEEM（持续层次化探索-利用记忆）。基于Tiny与Base两类ViT视觉Transformer骨干网络，在具有挑战性的MTIL和VDD基准测试上，结合所提出的综合品质因数（FoM）度量进行评估，CHEEM显著优于当前最优的提示式持续学习方法，接近全微调上限。此外，它能以语义有意义的方式为各任务学习自适应模型结构。我们的代码开源在 https://github.com/savadikarc/cheem 。