22 citations · 73 across the 29 of their papers we have counts for
4 papers · 1 filter
DETER: Detecting Edited Regions for Deterring Generative Manipulations
Sai Wang, Ye Zhu, Ruoyu Wang +3
Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or "deep fakes". However, deep fake d…
A Sampling-Based Domain Generalization Study with Diffusion Generative Models
Ye Zhu, Yu Wu, Duo Xu +3
In this work, we investigate the domain generalization capabilities of diffusion models in the context of synthesizing images that are distinct from the training data. Instead of f…
Vision-Language Dataset Distillation
Xindi Wu, Byron Zhang, Zhiwei Deng +1
Dataset distillation methods reduce large-scale datasets to smaller sets of synthetic data, preserving sufficient information to quickly train a new model from scratch. However, pr…
Boundary Guided Learning-Free Semantic Control with Diffusion Models
Ye Zhu, Yu Wu, Zhiwei Deng +2
Applying pre-trained generative denoising diffusion models (DDMs) for downstream tasks such as image semantic editing usually requires either fine-tuning DDMs or learning auxiliary…