Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

Rapid advances in artificial intelligence (AI) in the last decade have been largely built upon
the wide applications of deep learning (DL). However, the high carbon footprint yielded by …

With the rising industrial attention to 3D virtual modeling technology, generating novel 3D
content based on specified conditions (eg text) has become a hot issue. In this paper, we …

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

This work presents a systematic benchmark of differentially private synthetic data generation
algorithms that can generate tabular data. Utility of the synthetic data is evaluated by …

Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple
2D input views. However, a well-known drawback of NeRFs is the less-than-ideal …

Abstract Best-of-N (BoN) Average Displacement Error (ADE)/Final Displacement Error (FDE)
is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not …

Despite their recent successes, generative adversarial networks (GANs) for semantic image
synthesis still suffer from poor image quality when trained with only adversarial supervision …

Abstract Generative Adversarial Networks (GANs) produce impressive results on
unconditional image generation when powered with large-scale image datasets. Yet …

This work introduces an optimal transportation (OT) view of generative adversarial networks
(GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold …