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

Real-world datasets are often biased with respect to key demographic factors such as race
and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias …

Abstract Generative Adversarial Networks (GANs) are one of the most practical methods for
learning data distributions. A popular GAN formulation is based on the use of Wasserstein …

Image extension models have broad applications in image editing, computational
photography and computer graphics. While image inpainting has been extensively studied …

Training generative adversarial networks requires balancing of delicate adversarial
dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with …

Generative Adversarial Networks (GANs) have shown impressive results for image
generation. However, GANs face challenges in generating contents with certain types of …

We begin with the hypothesis that a model must be able to understand individual objects
and relationships between objects in order to generate complex scenes with multiple objects …

In this paper, we present DRANet, a network architecture that disentangles image
representations and transfers the visual attributes in a latent space for unsupervised cross …

We present a deep learning solution for estimating the incident illumination at any 3D
location within a scene from an input narrow-baseline stereo image pair. Previous …

Generating realistic images of complex visual scenes becomes challenging when one
wishes to control the structure of the generated images. Previous approaches showed that …