GUIDE: Guidance-based Incremental Learning with Diffusion Models

Bartosz Cywiński, Kamil Deja, Tomasz Trzcinski, Bartłomiej Twardowski, Łukasz Kuciński

Keywords: continual learning, diffusion models

TL;DR: GUIDE: novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten.

Abstract:

We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten.
Existing generative strategies combat catastrophic forgetting by randomly sampling rehearsal examples from a generative model. Such an approach contradicts buffer-based approaches where sampling strategy plays an important role.
We propose to bridge this gap by incorporating classifier guidance into the diffusion process to produce rehearsal examples specifically targeting information forgotten by a continuously trained model. This approach enables the generation of samples from preceding task distributions, which are more likely to be misclassified in the context of recently encountered classes.
Our experimental results show that GUIDE significantly reduces catastrophic forgetting,
outperforming conventional random sampling approaches and surpassing recent state-of-the-art methods in continual learning with generative replay.

Primary Area: transfer learning, meta learning, and lifelong learning

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 9764