6 papers · 1 filter
Membership and Dataset Inference Attacks on Large Audio Generative Models
Jakub Proboszcz, PaweÅ Kochanski, Karol Korszun +5
Generative audio models, based on diffusion and autoregressive architectures, have advanced rapidly in both quality and expressiveness. This progress, however, raises pressing copy…
ELROND: Exploring and decomposing intrinsic capabilities of diffusion models
PaweÅ SkierÅ, Tomasz TrzciÅski, Kamil Deja
A single text prompt passed to a diffusion model often yields a wide range of visual outputs determined solely by stochastic process, leaving users with no direct control over whic…
Joint Diffusion models in Continual Learning
PaweÅ SkierÅ, 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…
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
Bartosz CywiÅski, Kamil Deja
Diffusion models, while powerful, can inadvertently generate harmful or undesirable content, raising significant ethical and safety concerns. Recent machine unlearning approaches o…
Low-Rank Continual Personalization of Diffusion Models
Åukasz 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 CywiÅski, Kamil Deja, Tomasz TrzciÅski +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…