Active perception and disentangled representations allow continual, episodic zero and few-shot learning

TL;DR

This paper introduces a novel Complementary Learning System architecture that enables continual, zero-shot, and few-shot learning by combining fast, disentangled perception with slow, generalized learning, avoiding interference and enhancing robustness.

Contribution

It proposes a parallel reasoning system with a fast, disentangled learner and a slow, statistical learner, facilitating continual zero and few-shot learning without relying on traditional generalization.

Findings

Fast learner overcomes observation variability

Contextual bias guides slow learner for generalization

System enables robust continual learning

Abstract

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.