Jun 10, 2014 · We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G.

This paper provides a comprehensive guide to GANs, covering their architecture, loss functions, training methods, applications, evaluation metrics, challenges, and future directions. We begin with an introduction to GANs and their historical development, followed by a review of the background and related work.

Jan 9, 2025 · There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim and build a modern GAN baseline in a more principled manner.

Apr 1, 2021 · In this study, the authors have presented a systematic review analysis on recent publications of GAN models and their applications. Three libraries such as Embase (Scopus), WoS, and PubMed have been considered for searching the relevant papers available in this area.

A GAN, or Generative Adversarial Network, is a generative model that simultaneously trains two models: a generative model G that captures the data distribution, and a discriminative model D …

Oct 27, 2017 · We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.

Mar 4, 2019 · Generative Adversarial Networks are one of the most interesting and popular applications of Deep Learning. This article will list 10 papers on GANs that will give you a great introduction...