Continual Learning in Open-vocabulary Classification with Complementary Memory Systems

Zhen Zhu, Weijie Lyu, Yao Xiao, Derek Hoiem

Primary Area: transfer learning, meta learning, and lifelong learning

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Keywords: Continual learning; Open-vocabulary classification; Complementary learning systems

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TL;DR: A responsive, flexible, efficient complementary learning system for open-vocabulary continual learning

Abstract: We introduce a method for flexible and efficient continual learning in open-vocabulary image classification, drawing inspiration from the complementary learning systems observed in human cognition. Specifically, we propose to combine predictions from a CLIP zero-shot model and the exemplar-based model, using the zero-shot estimated probability that a sample's class is within the exemplar classes. We also propose a "tree probe" method, an adaption of lazy learning principles, which enables fast learning from new examples with competitive accuracy to batch-trained linear models. We test in data incremental, class incremental, and task incremental settings, as well as ability to perform flexible inference on varying subsets of zero-shot and learned categories. Our proposed method achieves a good balance of learning speed, target task effectiveness, and zero-shot effectiveness.

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Submission Number: 1611