Dream2Learn: 结构化生成式做梦用于持续学习
Dream2Learn: Structured Generative Dreaming for Continual Learning
AI总结：
Dream2Learn通过生成梦中类别来提升持续学习的适应性，利用内部合成数据自我训练，实现更好的知识迁移和泛化能力。
摘要：
持续学习需要在保持稳定性与可塑性之间取得平衡，同时缓解灾难性遗忘。受人类做梦作为内部模拟和知识重构机制的启发，我们引入了Dream2Learn（D2L）框架，其中模型能够自主地从自身内部表示中生成结构化的合成经验，并利用这些经验进行自我提升。与生成重放中重建过去数据不同，D2L使分类器能够创建新颖、语义不同的梦中类别，这些类别与所学知识一致但不对应于之前观察到的数据。这些梦中的样本是通过冻结扩散模型并利用分类器自身驱动的软提示优化进行条件生成的。生成的数据不用于替代记忆，而是用于扩展和重新组织表示空间，从而有效使网络能够对内部合成的概念进行自我训练。通过将梦中类别整合到持续训练中，D2L主动结构化潜在特征以支持向前的知识转移和对后续任务的适应。这种前瞻性的自我训练机制模仿了睡眠在巩固和重组记忆中的作用，将内部模拟转化为提高泛化能力的工具。在Mini-ImageNet、FG-ImageNet和ImageNet-R上的实验表明，D2L在持续学习任务中始终优于强大的重放基线，并实现了积极的向前转移，证实了其通过内部生成的训练信号增强适应能力的能力。
英文摘要：
Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowledge restructuring, we introduce Dream2Learn (D2L), a framework in which a model autonomously generates structured synthetic experiences from its own internal representations and uses them for self-improvement. Rather than reconstructing past data as in generative replay, D2L enables a classifier to create novel, semantically distinct dreamed classes that are coherent with its learned knowledge yet do not correspond to previously observed data. These dreamed samples are produced by conditioning a frozen diffusion model through soft prompt optimization driven by the classifier itself. The generated data are not used to replace memory, but to expand and reorganize the representation space, effectively allowing the network to self-train on internally synthesized concepts. By integrating dreamed classes into continual training, D2L proactively structures latent features to support forward knowledge transfer and adaptation to future tasks. This prospective self-training mechanism mirrors the role of sleep in consolidating and reorganizing memory, turning internal simulations into a tool for improved generalization. Experiments on Mini-ImageNet, FG-ImageNet, and ImageNet-R demonstrate that D2L consistently outperforms strong rehearsal-based baselines and achieves positive forward transfer, confirming its ability to enhance adaptability through internally generated training signals.
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。
