Selected from KDnuggets Translated by Machine Heart Author:Michael Dietz Contributors: Jane W, Yan Qi, Wu Pan Generative Adversarial Networks (GANs) have gained significant attention in the research community recently. In this article, Michael Dietz, founder of Waya.ai, explains why GANs hold such potential and illustrates how GANs work through a vivid comparison with boxing matches. … Read more
In the previous article, someone raised the question in a comment about whether generative adversarial networks could be used to simulate shooting covers. This was previously tested in a simple experiment, but the results were not ideal. The unsatisfactory results stem from the implementation of general generative adversarial networks, which essentially involves a transformation or … Read more
Author丨Guo Xiaofeng Affiliation丨iQIYI Research Area丨Image Generation Recently, while studying GANs, I found that most of the current overview articles on GANs are from 2016 by Ian Goodfellow or from Professor Wang Feiyue of the Automation Institute. However, in the field of deep learning and GANs, progress is measured in months, and those two overviews feel … Read more
Author: Mirko Translated by: Sour Bun Wishing you a peaceful Dragon Boat Festival Generative Adversarial Network Applications in Quantitative Investing Series (Part 1) Get the complete code at the end of the article 1 Introduction Overfitting is one of the challenges we face when applying machine learning techniques to time series. This issue arises because … Read more
Introduction GAN, short for Generative Adversarial Networks, is a type of generative model. Personally, I like to call it the “involution” network. Why do I say this? Let’s start with a story!!! 01 The Story of Cops and Robbers On a distant planet in the universe, there is a city that is emerging, with various … Read more
Click the card below to follow the “Computer VisionDaily” public account AI/CV heavy content delivered promptly Reprinted from: Machine Heart | Edited by: Du Wei, Chen Ping An input facial image can actually generate diverse styles of anime characters. Researchers from the University of Illinois at Urbana-Champaign have achieved this with a novel GAN transfer … Read more
1. Introduction Generative Adversarial Networks (GAN) is a deep learning model framework proposed by Ian Goodfellow and his team in 2014, first published in the paper “Generative Adversarial Networks”. Before the rise of deep learning, the main research directions for generative models included probabilistic graphical models (such as Hidden Markov Models (HMM)), variational inference methods … Read more
With the rapid development of deep learning, significant progress has also been made in the field of generative models. Generative Adversarial Networks (GAN) are an unsupervised learning method proposed based on the theory of two-player zero-sum games in game theory. GAN consists of a generator network and a discriminator network, and is trained through adversarial … Read more
The powerful feature representation and nonlinear fitting capability of deep neural networks stem from sufficient learning on high-quality datasets. However, in practical engineering applications, due to economic and labor costs, acquiring a large amount of typical labeled data becomes extremely challenging, resulting in avery limited number of training samples. Data augmentation methods provide a simple … Read more
Source: https://en.wikipedia.org/wiki/Edmond_de_Belamy Five years ago, Generative Adversarial Networks (GANs) revolutionized the field of deep learning. This revolution led to significant technological breakthroughs. Ian Goodfellow and others proposed GANs in “Generative Adversarial Networks.” The academic and industrial sectors began to embrace and welcome the arrival of GANs. The rise of GANs was inevitable. Firstly, the most … Read more