Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …

Large-scale text-to-image diffusion models have made amazing advances. However, the
status quo is to use text input alone, which can impede controllability. In this work, we …

With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

Abstract 3D-aware generative models have shown that the introduction of 3D information
can lead to more controllable image generation. In particular, the current state-of-the-art …

Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this
paper turns it into a photorealistic image-just like those shown in Fig. 1 (a), all non-cherry …

This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial
autoencoders, and their variants. We start with explaining adversarial learning and the …

While generative adversarial networks (GANs) can successfully produce high-quality
images, they can be challenging to control. Simplifying GAN-based image generation is …

Neural networks are prone to learning shortcuts--they often model simple correlations,
ignoring more complex ones that potentially generalize better. Prior works on image …

Rapid progress in text-to-image generation has been often measured by Frechet Inception
Distance (FID) to capture how realistic the generated images are, or by R-Precision to …

Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …