Learning to Prompt Knowledge Transfer for Open-World Continual Learning

• URL: http://arxiv.org/abs/2312.14990v1
• Date: Fri, 22 Dec 2023 11:53:31 GMT
• Title: Learning to Prompt Knowledge Transfer for Open-World Continual Learning
• Authors: Yujie Li, Xin Yang, Hao Wang, Xiangkun Wang and Tianrui Li
• Abstract summary: Pro-KT is a novel prompt-enhanced knowledge transfer model for Open-world Continual Learning. 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.
• Score: 13.604171414847531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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