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arXiv · 2306.12026

Continual Learners are Incremental Model Generalizers

Jaehong Yoon·Sung Ju Hwang·Yue Cao
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Abstract

Motivated by the efficiency and rapid convergence of pre-trained models for solving downstream tasks, this paper extensively studies the impact of Continual Learning (CL) models as pre-trainers. In both supervised and unsupervised CL, we find that the transfer quality of the representation often increases gradually without noticeable degradation in fine-tuning performance. This is because CL models can learn improved task-general features when easily forgetting task-specific knowledge. Based on this observation, we suggest a new unsupervised CL framework with masked modeling, which aims to capture fluent task-generic representation during training. Furthermore, we propose a new fine-tuning scheme, GLobal Attention Discretization (GLAD), that preserves rich task-generic representation during solving downstream tasks. The model fine-tuned with GLAD achieves competitive performance and can also be used as a good pre-trained model itself. We believe this paper breaks the barriers between pre-training and fine-tuning steps and leads to a sustainable learning framework in which the continual learner incrementally improves model generalization, yielding better transfer to unseen tasks.

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Jaehong Yoon, Sung Ju Hwang, Yue Cao. 2023-06-21. Continual Learners are Incremental Model Generalizers. https://arxiv.org/abs/2306.12026

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