6 citations · 31 across the 18 of their papers we have counts for
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Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
Yize Li, Yihua Zhang, Sijia Liu +1
Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to a…
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
Yimeng Zhang, Xin Chen, Jinghan Jia +6
Diffusion models (DMs) have achieved remarkable success in text-to-image generation, but they also pose safety risks, such as the potential generation of harmful content and copyri…
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
Can Jin, Tianjin Huang, Yihua Zhang +4
The rapid development of large-scale deep learning models questions the affordability of hardware platforms, which necessitates the pruning to reduce their computational and memory…
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now
Yimeng Zhang, Jinghan Jia, Xin Chen +5
The recent advances in diffusion models (DMs) have revolutionized the generation of realistic and complex images. However, these models also introduce potential safety hazards, suc…