Active perception and disentangled representations allow continual, episodic zero and few-shot learning
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| Format: | Preprint |
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2026
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| _version_ | 1866908848397221888 |
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| author | Rawlinson, David Kowadlo, Gideon |
| author_facet | Rawlinson, David Kowadlo, Gideon |
| contents | Generalization is often regarded as an essential property of machine learning systems. However, perhaps not every component of a system needs to generalize. Training models for generalization typically produces entangled representations at the boundaries of entities or classes, which can lead to destructive interference when rapid, high-magnitude updates are required for continual or few-shot learning. Techniques for fast learning with non-interfering representations exist, but they generally fail to generalize. Here, we describe a Complementary Learning System (CLS) in which the fast learner entirely foregoes generalization in exchange for continual zero-shot and few-shot learning. Unlike most CLS approaches, which use episodic memory primarily for replay and consolidation, our fast, disentangled learner operates as a parallel reasoning system. The fast learner can overcome observation variability and uncertainty by leveraging a conventional slow, statistical learner within an active perception system: A contextual bias provided by the fast learner induces the slow learner to encode novel stimuli in familiar, generalized terms, enabling zero-shot and few-shot learning. This architecture demonstrates that fast, context-driven reasoning can coexist with slow, structured generalization, providing a pathway for robust continual learning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_19355 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Active perception and disentangled representations allow continual, episodic zero and few-shot learning Rawlinson, David Kowadlo, Gideon Machine Learning Artificial Intelligence 68Q05 68T05 90C40 68T30 92C20 I.2.6; I.2.4; F.1.1; I.2.8 Generalization is often regarded as an essential property of machine learning systems. However, perhaps not every component of a system needs to generalize. Training models for generalization typically produces entangled representations at the boundaries of entities or classes, which can lead to destructive interference when rapid, high-magnitude updates are required for continual or few-shot learning. Techniques for fast learning with non-interfering representations exist, but they generally fail to generalize. Here, we describe a Complementary Learning System (CLS) in which the fast learner entirely foregoes generalization in exchange for continual zero-shot and few-shot learning. Unlike most CLS approaches, which use episodic memory primarily for replay and consolidation, our fast, disentangled learner operates as a parallel reasoning system. The fast learner can overcome observation variability and uncertainty by leveraging a conventional slow, statistical learner within an active perception system: A contextual bias provided by the fast learner induces the slow learner to encode novel stimuli in familiar, generalized terms, enabling zero-shot and few-shot learning. This architecture demonstrates that fast, context-driven reasoning can coexist with slow, structured generalization, providing a pathway for robust continual learning. |
| title | Active perception and disentangled representations allow continual, episodic zero and few-shot learning |
| topic | Machine Learning Artificial Intelligence 68Q05 68T05 90C40 68T30 92C20 I.2.6; I.2.4; F.1.1; I.2.8 |
| url | https://arxiv.org/abs/2602.19355 |