Geometric GAN

【Geometric GAN】的更多相关文章

目录概主要内容 McGAN 结合SVM 训练训练理论分析证明 Jae Hyun Lim, Jong Chul Ye, Geometric GAN. 概很有趣, GAN的训练过程可以分成寻找一个超平面区分real和fake; 训练判别器, 使得real和fake分得更开; 训练生成器, 使得real趋向错分一侧. 主要内容 McGAN 本文启发自McGAN, 在此基础上, 有了下文. 结合SVM 设想, GAN的判别器\(D(x) = S(\langle w, \Phi_{\zeta…

[转]GAN论文集

really-awesome-gan A list of papers and other resources on General Adversarial (Neural) Networks. This site is maintained by Holger Caesar. To complement or correct it, please contact me at holger-at-it-caesar.com or visit it-caesar.com. Also checkou…

语音合成论文翻译：2019_MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

论文地址:MelGAN:条件波形合成的生成对抗网络代码地址:https://github.com/descriptinc/melgan-neurips 音频实例:https://melgan-neurips.github.io/ 配有MelGAN解码器的音乐翻译网络:https://www.descript.com/overdub 摘要以前的工作(Donahue等人,2018a:Engel等人,2019a)已经发现用GAN生成相干的原始音频波形是一个挑战.在本文中,我们证明了通过引入一系列结…

(转) How to Train a GAN? Tips and tricks to make GANs work

How to Train a GAN? Tips and tricks to make GANs work 转自:https://github.com/soumith/ganhacks While research in Generative Adversarial Networks (GANs) continues to improve the fundamental stability of these models, we use a bunch of tricks to train th…

不要怂，就是GAN (生成式对抗网络) （一）

前面我们用 TensorFlow 写了简单的 cifar10 分类的代码,得到还不错的结果,下面我们来研究一下生成式对抗网络 GAN,并且用 TensorFlow 代码实现. 自从 Ian Goodfellow 在 14 年发表了论文 Generative Adversarial Nets 以来,生成式对抗网络 GAN 广受关注,加上学界大牛 Yann Lecun 在 Quora 答题时曾说,他最激动的深度学习进展是生成式对抗网络,使得 GAN 成为近年来在机器学习领域的新宠,可以说,研究机器…

基本概率分布Basic Concept of Probability Distributions 3: Geometric Distribution

PDF version PMF Suppose that independent trials, each having a probability $p$, $0 < p < 1$, of being a success, are performed until a success occurs. If we let $X$ equal the number of failures required, then the geometric distribution mass function…

geometric median

The geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points. This generalizes the median, which has the property of minimizing the sum of distances for one-dimensional…