Editor’s Note In order to deepen the understanding of the relevant terms in the “Decision of the Central Committee of the Communist Party of China on Further Deepening Reform and Promoting Chinese-style Modernization” adopted at the 20th Central Committee’s Third Plenary Session among the vast number of party members and cadres, the Central Party School … Read more
Table of Contents What is GAN? What Can GAN Do? Framework and Training of GAN Similarities and Differences Between GAN and Other Generative Models Existing Problems of GAN Models (Continued from last issue) 3 Framework and Training of GAN Previously, we mentioned that GAN consists mainly of two parts: the generator model and the discriminator … Read more
Introduction The GANpaint tool developed by MIT allows for easy photo editing, enabling users to upload any photo for editing without damaging its original details. In addition to helping artists and designers quickly adjust visual effects, researchers say this work may help computer scientists identify “fake” images. While we are still immersed in the black … Read more
Introduction In today’s article, we will implement a machine learning model that can generate countless similar image samples based on a given dataset. To achieve this machine learning model, we will launch Generative Adversarial Networks (GANs) and input data containing features of “The Simpsons” images. By the end of this article, you will be familiar … Read more
Click the "Xiaobai Learns Vision" above, select "Add to Favorites" or "Pin" Heavyweight content delivered first time Introduction This article explains in detail how the minimax game and total loss function in GAN optimization functions are derived. It will introduce the meaning and reasoning of the optimization function in the original GAN, as well as … Read more
Source: Machine Heart Author: Jiang Siyuan The length of this article is 8300 words, recommended reading time is 8 minutes This article will start from the original paper, using Goodfellow’s speech at NIPS 2016 and Li Hongyi’s explanation from National Taiwan University, to complete the derivation, proof, and implementation of the original GAN. This article … Read more
On September 17, Professor Fan Lei from Capital Normal University shared a presentation titled “Generative Adversarial Networks – Intuitive Principles and Simple Applications” at the Yuanzhuo Academy. He introduced the basic concepts of Generative Adversarial Networks (GANs) and constructed a simple GAN model to help everyone understand the memory of familiar problems. Professor Fan introduced … Read more
Click above to subscribe to “China Computer Federation” easily! Source: “China Computer Federation Communication”, 2017, Issue 11, “Column” GAN: Generative Adversarial Networks Generative Adversarial Networks (GANs) are a concept proposed by Ian Goodfellow in 2014 that uses adversarial methods to generate data. Imagine we have two images, one real and one fake. How can humans … Read more
Original Title: An Intuitive Introduction to Generative Adversarial Networks Authors: Keshav Dhandhania, Arash Delijani Translation: Shen Libin Proofreading: He Zhonghua This article is about 4000 words and is recommended to be read in 10 minutes. The article introduces the GAN model through the problem of image generation, discusses the mathematical principles and training process of … Read more
