arXiv cs.AI· David Rawlinson, Gideon Kowadlo·· 14 小时前AI 评分22
基于强化学习偏置感知的“情景式观察”方法提升小样本与持续学习并防止遗忘
Episodic looking - using reinforcement learning to bias perception enhances few-shot and continual learning with protection from forgetting
AI 导读
研究者提出基于互补学习系统（CLS）架构的方法，让短期记忆在长期记忆产出的泛化感知空间中工作，从而专注记忆近期输入并减少记忆间干扰。在图像分类任务上，该CLS模型在域增量持续学习中的抗遗忘能力优于基线ResNet长期记忆，小样本场景下的泛化效果也优于其他持续学习模型。
正文 · 原文
Abstract:There is a fundamental contradiction between the desire for models that can generalize, and models that can continuously adapt to recent input with changing statistics. These conditions, captured in few-shot and continual learning settings, can bias or disrupt learned representations. This harms generalization to a broader context, leaving the model unable to overcome input variance.
This paper proposes a solution based on the Complementary Learning System architecture (CLS), in which a Short-Term Memory (STM) delegates generalization to a slow, statistical Long-Term Memory (LTM). Delegation is achieved by working in a space of robust, generalized perceptions produced by the LTM. With this separation, the STM can focus on memorizing recent input and avoiding interference between memories.
We show that on an image classification task, the resulting CLS model is more resistant to forgetting in a domain-incremental continual learning setting than a baseline ResNet LTM, and generalizes more effectively in a few-shot setting than other continual learners.
In the continual setting, overall accuracy is slightly worse than existing continual learning models, but with a different mix of performance characteristics. Results analysis suggests that the model is limited by the representations and representational plasticity of the LTM. In future work, we will seek to address this by replaying STM memories to the LTM.
| Comments: | 21 pages, 8 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| MSC classes: | 68T05 (Primary) 68T07, 68Q32 (Secondary) |
| ACM classes: | I.2.6; I.2.6; I.5.1 |
| Cite as: | arXiv:2610.38577 [cs.LG] |
| (or arXiv:2610.38577v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38577 arXiv-issued DOI via DataCite |
Submission history
From: David Rawlinson [view email]
[v1]
Wed, 28 Oct 2026 10:09:11 UTC (1,824 KB)
来源：arXiv cs.AI · arxiv.org