首先从互信息定义的角度,互信息公式为: 因此,当 X Y 彼此独立时,互信息是等于 0 的 而通过上述公式与 KL 散度的定义就可以发现互信息是可以写作联合分布与分布乘积的 KL 形式,这个不太重要,自己推一下就很容易推出来。 但对于下界问题,文章里并没有给出进一步解释,其实是可以去翻 MINE 里的公式,还比较好理...
Learning deep representations by mutual information estimation and maximizationarxiv.org/abs/1808.06670 Representation Learning with Contrastive Predictive Codingarxiv.org/abs/1807.03748 概览: 第一篇文章MINE,是MILA在ICML 2018上发表的论文,提出了一个新的mutual information的neural estimator, MINE, 并...
Learning deep representations by mutual information estimation and maximization Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, Yoshua Bengio ICLR 2019|April 2019 Organized by ICLR Download BibTex ...
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a...
【论文笔记——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization,程序员大本营,技术文章内容聚合第一站。
We’re optimistic this is only the beginning for how DIM can help researchers advance machine learning by providing a more effective way to learn good representations. (在新选项卡中打开) 相关论文与出版物 Deep Graph Infomax Learning deep representations by mutual informati...
4.3 LEARNING DEEP REPRESENTATIONS BY MUTUAL IN- FORMATION ESTIMATION AND MAXIMIZATION (Deep InfoMax) 不重复造轮子之 paper 讲解传送门: One Sentence Summary:加入了更细致的 local feature 与 output 的互信息,加上了先验,让先验服从一定的分布,从而能更好的具有编码空间。
Learning Deep Representations by Mutual Information Estimation and Maximization Sample code to do the local-only objective inhttps://openreview.net/forum?id=Bklr3j0cKXhttps://arxiv.org/abs/1808.06670 Completed [Updated 4/12/2019] Latest code for dot-product style scoring function for local DIM (...
通过上面的分析和推导,我们有了这样一个通用的框架,那么 deep infomax 这篇文章就非常好理解了,其中正样本就是第 i 张图片的 global feature 和中间 feature map 上个的 local feature,而负样本就是另外一张图片作为输入,非常好理解。 source: [Le...
LEARNING DEEP REPRESENTATIONS BY MUTUAL INFORMATION ESTIMATION AND MAXIMIZATION(从生成对抗网络思考DIM) 随遇而安 做学术,搞科研,爱打球~~~4 人赞同了该文章 原文地址:arxiv.org/pdf/1808.0667编辑于 2022-03-07 12:46 深度学习(Deep Learning) 机器学习 特征提取 ...