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 …

This work is an update of my previous paper on the same topic published a few years ago
(Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative …

We consider the problem of reconstructing a full 360deg photographic model of an object
from a single image of it. We do so by fitting a neural radiance field to the image, but find this …

Guided image synthesis enables everyday users to create and edit photo-realistic images
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Denoising diffusion probabilistic models (DDPMs) have achieved high quality image
generation without adversarial training, yet they require simulating a Markov chain for many …

Generative adversarial networks (GANs) have recently found applications in image editing.
However, most GAN-based image editing methods often require large-scale datasets with …

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 …

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …

Recent work has shown that a variety of semantics emerge in the latent space of Generative
Adversarial Networks (GANs) when being trained to synthesize images. However, it is …