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What is Synthetic Data for Class-Incremental Learning about?

This paper presents a novel approach to class-incremental learning using a pretrained Stable Diffusion model to generate synthetic samples for knowledge distillation and replay. The method aims to mitigate catastrophic forgetting by leveraging additional labeled data that corresponds to previously learned classes, enhancing the performance of existing state-of-the-art techniques. Experimental results on benchmarks like CIFAR100 and ImageNet demonstrate the effectiveness of this approach compared to traditio

Author

tasks888999

Language

EN