1 citations · 1 across the 8 of their papers we have counts for
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Image Generation Diversity Issues and How to Tame Them
Mischa Dombrowski, Weitong Zhang, Sarah Cechnicka +2
Generative methods now produce outputs nearly indistinguishable from real data but often fail to fully capture the data distribution. Unlike quality issues, diversity limitations i…
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski +3
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion…
Data-Efficient Generation for Dataset Distillation
Zhe Li, Weitong Zhang, Sarah Cechnicka +1
While deep learning techniques have proven successful in image-related tasks, the exponentially increased data storage and computation costs become a significant challenge. Dataset…
URCDM: Ultra-Resolution Image Synthesis in Histopathology
Sarah Cechnicka, James Ball, Matthew Baugh +6
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods…
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
Weitong Zhang, Chengqi Zang, Liu Li +3
Inverse problems describe the process of estimating the causal factors from a set of measurements or data. Mapping of often incomplete or degraded data to parameters is ill-posed,…