1 citations · 2 across the 13 of their papers we have counts for
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Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
Joanna Kaleta, Paweł Skierś, Jan Dubiński +2
We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique chal…
Joint Diffusion models in Continual Learning
Paweł Skierś, Kamil Deja
In this work, we introduce JDCL - a new method for continual learning with generative rehearsal based on joint diffusion models. Neural networks suffer from catastrophic forgetting…
Low-Rank Continual Personalization of Diffusion Models
Łukasz Staniszewski, Katarzyna Zaleska, Kamil Deja
Recent personalization methods for diffusion models, such as Dreambooth and LoRA, allow fine-tuning pre-trained models to generate new concepts. However, applying these techniques…
GUIDE: Guidance-based Incremental Learning with Diffusion Models
Bartosz Cywiński, Kamil Deja, Tomasz Trzciński +2
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten. Existing generative strategies combat catastro…