Continual Learning of Diffusion Models with Generative Distillation
Abstract
Diffusion models are powerful generative models that achieve state-of-the-art performance in image synthesis. However, training them demands substantial amounts of data and computational resources. Continual learning would allow for incrementally learning new tasks and accumulating knowledge, thus enabling the reuse of trained models for further learning. One potentially suitable continual learning approach is generative replay, where a copy of a generative model trained on previous tasks produces synthetic data that are interleaved with data from the current task. However, standard generative replay applied to diffusion models results in a catastrophic loss in denoising capabilities. In this paper, we propose generative distillation, an approach that distils the entire reverse process of a diffusion model. We demonstrate that our approach substantially improves the continual learning performance of generative replay with only a modest increase in the computational costs.
Cite
@article{arxiv.2311.14028,
title = {Continual Learning of Diffusion Models with Generative Distillation},
author = {Sergi Masip and Pau Rodriguez and Tinne Tuytelaars and Gido M. van de Ven},
journal= {arXiv preprint arXiv:2311.14028},
year = {2025}
}
Comments
To appear in the Proceedings of the Third Conference on Lifelong Learning Agents (CoLLAs), 2024