Generative artificial intelligence (AI) has emerged as a powerful technology with numerous
applications in various domains. There is a need to identify the requirements and evaluation …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Generating and editing images from open domain text prompts is a challenging task that
heretofore has required expensive and specially trained models. We demonstrate a novel …

Text-guided image editing is widely needed in daily life, ranging from personal use to
professional applications such as Photoshop. However, existing methods are either zero …

We present a method for zero-shot, text-driven editing of natural images and videos. Given
an image or a video and a text prompt, our goal is to edit the appearance of existing objects …

Inspired by the ability of StyleGAN to generate highly re-alistic images in a variety of
domains, much recent work hasfocused on understanding how to use the latent spaces …

In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

In this paper, we focus on generating realistic images from text descriptions. Current
methods first generate an initial image with rough shape and color, and then refine the initial …

Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …