5 citations · 11 across the 14 of their papers we have counts for
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cs.CV2023★ 3 cited
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley +2
While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high…
cs.LG2023
The Score-Difference Flow for Implicit Generative Modeling
Romann M. Weber
Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work (e.g. score-matching networks,…
cs.CV2023
Controllable Inversion of Black-Box Face Recognition Models via Diffusion
Manuel Kansy, Anton Raël, Graziana Mignone +4
Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be di…