5 papers · 1 filter
When Preference Labels Fall Short: Aligning Diffusion Models from Real Data
Weiyan Chen, Weijian Deng, Yao Xiao +5
Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existing approaches rely on prefere…
Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection
Yao Xiao, Weiyan Chen, Jiahao Chen +8
Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convinc…
Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
Yiming Liu, Kezhao Liu, Yao Xiao +4
Diffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks. The success of DBP is often attributed to the forward diffusion proces…
NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results
Zheng Chen, Zongwei Wu, Eduard Zamfir +85
This paper reviews the NTIRE 2024 challenge on image super-resolution (4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating…
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Conghan Yue, Zhengwei Peng, Junlong Ma +3
Diffusion models exhibit powerful generative capabilities enabling noise mapping to data via reverse stochastic differential equations. However, in image restoration, the focus is…