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Found 5 papers, 0 papers with code
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
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 classifier-free guidance scale for optimal image quality or when trained on small datasets.
Ranked #3 on
Image Generation
on ImageNet 256x256
The Score-Difference Flow for Implicit Generative Modeling
Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution.
Controllable Inversion of Black-Box Face Recognition Models via Diffusion
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 distinguished from one another.
Exploiting the Hidden Tasks of GANs: Making Implicit Subproblems Explicit
We present an alternative perspective on the training of generative adversarial networks (GANs), showing that the training step for a GAN generator decomposes into two implicit subproblems.
Disentangled Dynamic Representations from Unordered Data
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input.
