arXiv 2401.116672025-09-03cs.LG

INCPrompt：面向无排练类增量学习的任务感知增量提示

INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning

Tsinghua Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China（平安科技（深圳）有限公司）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室）

Zhiyuan Wang, Xiaoyang Qu, Jing Xiao, Bokui Chen, Jianzong Wang

备注：: Accepted by the 49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本研究提出一种名为INCPrompt的无排练类增量持续学习方案，通过自适应键学习器与任务感知提示结合的方式，在多个基准上验证其能有效缓解灾难性遗忘并保持高性能。

摘要：

本文提出了INCPrompt，这是一种可有效解决灾难性遗忘问题的创新持续学习方案。INCPrompt的核心创新在于采用自适应键学习器与任务感知提示来捕捉任务相关信息。这种独特组合封装了跨任务的通用知识，并编码了任务特定知识。我们在多个持续学习基准上开展的综合评估表明，INCPrompt优于现有算法，能在缓解灾难性遗忘的同时保持高性能。这些结果凸显了任务感知增量提示对持续学习性能的显著影响。

英文摘要：

This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. INCPrompt's key innovation lies in its use of adaptive key-learner and task-aware prompts that capture task-relevant information. This unique combination encapsulates general knowledge across tasks and encodes task-specific knowledge. Our comprehensive evaluation across multiple continual learning benchmarks demonstrates INCPrompt's superiority over existing algorithms, showing its effectiveness in mitigating catastrophic forgetting while maintaining high performance. These results highlight the significant impact of task-aware incremental prompting on continual learning performance.

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。