An Energy Sampling Replay-Based Continual Learning Framework

Continual Learning represents a significant challenge within the field of computer vision, primarily due to the issue of catastrophic forgetting that arises with sequential learning tasks. Among the array of strategies explored in current continual learning research, replay-based methods have shown notable effectiveness. In this paper, we introduce a novel Energy Sampling Replay-based (ESR) structure for image classification. This framework enhances the selection process of samples for replay by leveraging the energy distribution of the samples, thereby improving the effectiveness of memory samples during the replay phase and increasing accuracy. We have conducted extensive experiments across various continual learning methodologies and datasets. The results demonstrate that our approach effectively mitigates forgetting on CIFAR-10, CIFAR-100 and CIFAR-110 datasets by optimizing the replay strategy.