Class-Incremental Learning based on Label Generation

Yijia Shao; Yiduo Guo; Dongyan Zhao; Bing Liu

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arXiv · 2306.12619

Class-Incremental Learning based on Label Generation

Yijia Shao·Yiduo Guo·Dongyan Zhao·Bing Liu

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Abstract

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.

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BibTeX RIS

Yijia Shao, Yiduo Guo, Dongyan Zhao, Bing Liu. 2023-06-22. Class-Incremental Learning based on Label Generation. https://arxiv.org/abs/2306.12619

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