English(EN)To Retain or to Adapt? Generalizing Continual Learning
新的arXiv论文探讨贝叶斯和概率方法用于持续学习 · 跟踪2个来源
作者PulseAugur 编辑部·[5 个来源]·
两篇新的arXiv论文探讨了持续学习的进展，这是一种允许AI模型顺序学习而不会忘记过去知识的方法。第一篇论文概述了持续学习的贝叶斯方法，讨论了它们与迁移学习和发展心理学等领域的联系。第二篇论文介绍了一个新颖的基于提示的框架，该框架将提示建模为概率分布，以捕获多样化的图像模式并防止提示崩溃，在ImageNet-R和CIFAR-100等基准测试中证明了其有效性。
AI
arXiv:2607.05609v1 Announce Type: new Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, often unstated assumption: that a lifelong learner should approximate the Joint-Task…
arXiv:2507.08922v3 Announce Type: replace Abstract: 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 with…
arXiv:2607.04711v1 Announce Type: new Abstract: Continual learning aims to progressively learn from a sequence of tasks, each containing a disjoint subset of classes, while preserving previously learned knowledge. Prompt-based continual learning methods propose to learn a small s…
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, often unstated assumption: that a lifelong learner should approximate the Joint-Task Learning (JTL) solution and retain all previous…
Continual learning aims to progressively learn from a sequence of tasks, each containing a disjoint subset of classes, while preserving previously learned knowledge. Prompt-based continual learning methods propose to learn a small set of parameters, i.e., prompts, by associating …
