译自:https://wiki.pathmind.com/generative-adversarial-network-gan 你可能认为编码者不是艺术家,但是编程是一个极具创意的职业。它是基于逻辑的创新。---John Romero 1、生成对抗网络定义(Generative Adversarial Network Definition) GANs是两个神经网络构成的算法架构,让一个网络与另一个网络竞争(因此称“对抗”(...
Jensen–Shannon divergence-Wiki Jensen-Shannon Divergence - Notes on AI Performance Metrics in Machine Learning Earth Mover's Distance (EMD) EMD is to measure the distance between the distribution, which can be 3D point cloud. In a certain feature space, EMD can evaluate the dissimilarity of two...
1. 什么是生成对抗网络(Generative Adversarial Network, GAN)? 对抗生成网络是当今计算机科学领域中最有趣的方向之一,通过两个模型相互对抗,同时训练以提升自身能力。这两个模型一个叫生成器(Generator),一个叫判别器(Discriminator),给定一组真实样本{xi}i=1n,生成器不断学习,以创造逼近训练样本的数据x^,而判别器...
Generative adversarial networks offer a novel method for data augmentation. We evaluate the use of CycleGAN for data augmentation in CT segmentation tasks. Using a large image database we trained a CycleGAN to transform contrast CT images into non-contrast images. We then used the trained CycleGAN...
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun Machine Learning for Healthcare (MLHC) 2017 Code Description This code trains a generative adversarial network to generate patient ...
https://skymind.ai/wiki/generative-adversarial-network-gan References Liben-Nowell D, Kleinberg J (2003) The link-prediction problem for social networks. Wiley subscription services, vol 58. A Wiley Company Inc, pp 556–559 Adamic L, Aar EA (2003) Friends and neighbors on the web. Soc Net...
矩阵范数:https://en.wikipedia.org/wiki/Matrix_norm 范数:https://en.wikipedia.org/wiki/Norm_(mathematics) 接下来提出的谱标准化,其实用到的就是矩阵的2-范式,对于矩阵W的谱标准化如下式: 其中,σ(W)表示的是W的二范式,如果对于分辨器D的每层权重W都做如上所示的谱标准化,那么将分辨器D看做一个函数...
Generative Adversarial Networks This repository contains the code and hyperparameters for the paper: "Generative Adversarial Networks." Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. ArXiv 2014. Please cite this pape...
Initially, Cycle-Generative Adversarial Network (CycleGAN) achieves the face age progression, further Enhanced Super-resolution Generative Adversarial Network (ESRGAN) automatically enhance the aged face image to improve the visibility. Simulation results on five face datasets, namely IMDB-WIKI, CACD and...
论文实验使用的数据集是IMDB-Wiki cleaned数据集(来源Grigory Antipov, Moez Baccouche, Sid-Ahmed Berrani, and Jean-Luc Dugelay, “Apparent age estimation from face images combining general and children-specialized deep learning models,” in Proceedings of Computer Vision and Pattern Recognition Workshops,...