Research on the improvement of image classification algorithm based on incremental learning

In the field of machine learning, incremental learning methods allow models to learn new tasks without forgetting the old ones, but the catastrophic forgetting problem often encountered in this process greatly limits their applications. In this paper, an improved algorithm EPASS (Enhanced Prototype Augmentation and Self-Supervision) is proposed for incremental learning in image classification tasks, which effectively alleviates the limitations of the traditional PASS algorithm and the EWC algorithm in terms of catastrophic forgetting, sensitivity of parameter selection, and computational cost. The EPASS algorithm improves the flexibility and adaptability of the model by automatically selecting which prototypes to keep for different tasks through a dynamic prototype storage strategy. Meanwhile, the EWC-based prototype update strategy allows prototypes to be updated as new data arrives, further improving the model's adaptability to new tasks while reducing computational cost. Experimental results on the CIFAR-100 dataset show that the EPASS algorithm outperforms the PASS algorithm to some extent in terms of accuracy at all stages. These results validate the effectiveness of the EPASS algorithm in mitigating the catastrophic forgetting problem and provide new directions for future incremental learning research.