Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

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 …

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …

Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from
given low-resolution (LR) images. It is an ill-posed problem because one LR image …

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 …

A good image-to-image translation model should learn a mapping between different visual
domains while satisfying the following properties: 1) diversity of generated images and 2) …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image
with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and …

Training generative models, such as GANs, on a target domain containing limited examples
(eg, 10) can easily result in overfitting. In this work, we seek to utilize a large source domain …

Numerous task-specific variants of conditional generative adversarial networks have been
developed for image completion. Yet, a serious limitation remains that all existing algorithms …