An Efficient Replay for Class-Incremental Learning with Pre-trained Models

• URL: http://arxiv.org/abs/2408.08084v1
• Date: Thu, 15 Aug 2024 11:26:28 GMT
• Title: An Efficient Replay for Class-Incremental Learning with Pre-trained Models
• Authors: Weimin Yin, Bin Chen adn Chunzhao Xie, Zhenhao Tan,
• Abstract summary: In class-incremental learning, the steady state among the weight guided by each class center is disrupted, which is significantly correlated with forgetting. We propose a new method to overcoming forgetting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In general class-incremental learning, researchers typically use sample sets as a tool to avoid catastrophic forgetting during continuous learning. At the same time, researchers have also noted the differences between class-incremental learning and Oracle training and have attempted to make corrections. In recent years, researchers have begun to develop class-incremental learning algorithms utilizing pre-trained models, achieving significant results. This paper observes that in class-incremental learning, the steady state among the weight guided by each class center is disrupted, which is significantly correlated with catastrophic forgetting. Based on this, we propose a new method to overcoming forgetting . In some cases, by retaining only a single sample unit of each class in memory for replay and applying simple gradient constraints, very good results can be achieved. Experimental results indicate that under the condition of pre-trained models, our method can achieve competitive performance with very low computational cost and by simply using the cross-entropy loss.

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