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
Click the “MLNLP” above to select the “star” public account Heavyweight content delivered promptly Source: Learning Notes of Qianmeng “ This article mainly introduces the recent developments and representative works of GAN (Generative Adversarial Networks) in distribution discrepancy measurement, IPM and regularization, dual learning, conditioning and control, resolution enhancement, evaluation metrics, etc. It is hoped … Read more
Mathematical Derivation of GAN Author: Sherlock Source: Machine Learning Algorithms and Natural Language Processing Previously, we discussed the basic idea of GAN. Recently, I reviewed some GAN papers and happened to watch a course by Professor Li Hongyi, finding the mathematical derivation quite interesting, so I decided to write it down for future reference. First, … Read more
Follow our public account to discover the beauty of CV technology This article shares a recent literature review on super-resolution titled ‘Generative Adversarial Networks for Image Super-Resolution: A Survey’. It discusses the performance, advantages, disadvantages, complexity, challenges, and potential research points of 193 related papers. Specific information is as follows: Authors: Tian Chunwei, Zhang Xuan … Read more
The “Outcome Overview” series of articles aims to disseminate important results from conferences and journals in the field of image graphics, allowing readers to quickly understand relevant academic dynamics in their native language through short articles. We welcome your attention and submissions~ ◆ ◆ ◆ ◆ GAN Review: Models and Medical Image Fusion Applications Zhou … Read more
