11 citations · 21 across the 10 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.CV2023
A High-Quality Robust Diffusion Framework for Corrupted Dataset
Quan Dao, Binh Ta, Tung Pham +1
Developing image-generative models, which are robust to outliers in the training process, has recently drawn attention from the research community. Due to the ease of integrating u…
cs.CV2023★ 6 cited
Flow Matching in Latent Space
Quan Dao, Hao Phung, Binh Nguyen +1
Flow matching is a recent framework to train generative models that exhibits impressive empirical performance while being relatively easier to train compared with diffusion-based m…
cs.CV2023★ 11 cited
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis
Thanh Van Le, Hao Phung, Thuan Hoang Nguyen +3
Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personal…