arXiv Computer Vision
Aug 25

Ordinal Diffusion Models for Color Fundus Images

The paper introduces an ordinal latent diffusion model for generating color fundus images that incorporates the ordered structure of diabetic retinopathy (DR) severity, using a scalar disease representation instead of categorical conditioning. Evaluations on the EyePACS dataset show improved visual realism, with reduced Fréchet inception distance for most stages and a higher quadratic weighted κ from 0.79 to 0.87. Interpolation experiments demonstrate the model captures a continuous spectrum of disease progression derived from coarse, ordered labels.

By Gustav Schmidt, Philipp Berens, Sarah M\"uller
arXiv AI
Jun 9

DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression

arXiv:2606. 07599v1 Announce Type: cross Abstract: Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision.

By Hongxu Ma, Lin Wang, Chenghou Jin, Han Zhou, Jie Zhang, Xiaoyu Yang, Chunjie Chen, Jihong Guan, Shuigeng Zhou
arXiv Machine Learning
Aug 10

Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions

arXiv:2409. 18804v3 Announce Type: replace-cross Abstract: Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used for image, audio, and video generation as well as many more applications in science and beyond.

By Iskander Azangulov, George Deligiannidis, Judith Rousseau
arXiv Machine Learning
Jul 14

CANDI: Hybrid Discrete-Continuous Diffusion Models

arXiv:2510. 22510v3 Announce Type: replace Abstract: While continuous diffusion has shown remarkable success in continuous domains such as image generation, its direct application to discrete data has underperformed pure discrete formulations.

By Patrick Pynadath, Jiaxin Shi, Ruqi Zhang