Continual Learning in Open-vocabulary Classification with Complementary Memory Systems

• URL: http://arxiv.org/abs/2307.01430v3
• Date: Sat, 05 Oct 2024 05:29:05 GMT
• Title: Continual Learning in Open-vocabulary Classification with Complementary Memory Systems
• Authors: Zhen Zhu, Weijie Lyu, Yao Xiao, Derek Hoiem,
• Abstract summary: We introduce a method for flexible and efficient continual learning in open-vocabulary image classification. We 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.
• Score: 19.337633598158778
• License:
• 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. Code will be available at https://github.com/jessemelpolio/TreeProbe.

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