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arXiv · 2312.14990

Learning to Prompt Knowledge Transfer for Open-World Continual Learning

Yujie Li·Xin Yang·Hao Wang·Xiangkun Wang·Tianrui Li
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Abstract

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly challenging in two-fold: i) learning a sequence of tasks without forgetting knowns in the past, and ii) identifying unknowns (novel objects/classes) in the future. Existing OwCL methods suffer from the adaptability of task-aware boundaries between knowns and unknowns, and do not consider the mechanism of knowledge transfer. In this work, we propose Pro-KT, a novel prompt-enhanced knowledge transfer model for OwCL. Pro-KT includes two key components: (1) a prompt bank to encode and transfer both task-generic and task-specific knowledge, and (2) a task-aware open-set boundary to identify unknowns in the new tasks. Experimental results using two real-world datasets demonstrate that the proposed Pro-KT outperforms the state-of-the-art counterparts in both the detection of unknowns and the classification of knowns markedly.

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Yujie Li, Xin Yang, Hao Wang, Xiangkun Wang, Tianrui Li. 2023-12-22. Learning to Prompt Knowledge Transfer for Open-World Continual Learning. https://arxiv.org/abs/2312.14990

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