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arXiv AI By David Rawlinson, Gideon Kowadlo

Episodic looking - using reinforcement learning to bias perception enhances few-shot and continual learning with protection from forgetting

Read the original on arXiv AI →

The paper addresses the conflict between generalization and continuous adaptation in few‑shot and continual learning. It proposes a Complementary Learning System (CLS) that separates a fast Short‑Term Memory (STM) for memorizing recent inputs from a slow Long‑Term Memory (LTM) that provides robust, generalized perceptions. Experiments on image classification show the CLS model resists forgetting in domain‑incremental continual learning and outperforms other continual learners in few‑shot settings, though overall accuracy is slightly lower than some existing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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