Diffusion models currently dominate the field of data-driven image synthesis with their
unparalleled scaling to large datasets. In this paper we identify and rectify several causes for …

Deep generative models allow for photorealistic image synthesis at high resolutions. But for
many applications, this is not enough: content creation also needs to be controllable. While …

Estimating counterfactual outcomes over time from observational data is relevant for many
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …

Despite recent progress in generative image modeling, successfully generating high-
resolution, diverse samples from complex datasets such as ImageNet remains an elusive …

Abstract We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning. Stochastic Weight …

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …

Recent work has shown local convergence of GAN training for absolutely continuous data
and generator distributions. In this paper, we show that the requirement of absolute …

Abstract While Generative Adversarial Networks (GANs) have seen huge successes in
image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due …

Despite their recent successes, GAN models for semantic image synthesis still suffer from
poor image quality when trained with only adversarial supervision. Historically, additionally …

We present a StyleGAN2‐based deep learning approach for 3D shape generation, called
SDF‐StyleGAN, with the aim of reducing visual and geometric dissimilarity between …