主动感知与解耦表示允许持续、事件式零样本和少样本学习
Active perception and disentangled representations allow continual, episodic zero and few-shot learning
AI总结：
本文提出一种互补学习系统，通过主动感知与解耦表示实现持续、零样本和少样本学习，无需依赖泛化能力。
摘要：
泛化通常被视为机器学习系统的重要属性。然而，也许系统中的并非每个组件都需要泛化。训练用于泛化的模型通常会在实体或类别的边界产生纠缠的表示，这可能导致在需要快速、高幅度更新以实现持续或少样本学习时产生破坏性干扰。存在用于快速学习的非干扰表示的技术，但它们通常无法泛化。在这里，我们描述了一个互补学习系统（CLS），其中快速学习器完全放弃泛化，以换取持续的零样本和少样本学习。与大多数CLS方法不同，我们的快速、解耦学习器将事件记忆主要用作并行推理系统。快速学习器可以通过利用一个常规的慢速、统计学习器来克服观察的变异性和不确定性，该学习器在主动感知系统中提供上下文偏差：由快速学习器提供的上下文偏差使慢速学习器能够以熟悉、泛化的方式编码新刺激，从而实现零样本和少样本学习。这种架构表明，快速、基于情境的推理可以与慢速、结构化的泛化共存，为稳健的持续学习提供了一条路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
