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,还比较好理解: 依次
This work investigates 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 in the input into the objective can significantl...
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 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 (...
Learning deep representations by mutual information estimation and maximization - DuaneNielsen/DeepInfomaxPytorch
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。
source: [Learning deep representations by mutual information estimation and maximization](https://arxiv.org/abs/1808.06670) Contrastive MultiView Coding 除了像上面这样去构建正负样本,还可以通过多模态的信息去构造,比如同一张图片的 RGB图 和 深度图。CMC 这篇 paper 就是从这一点出发去选择正样本,而且通过...
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 Deep Graph Infomax Continue reading December 6, 2024 Abstracts: NeurIPS 2024 with Dylan Foster November 11, 2024 Collaborators: Prompt engineering with Siddharth Suri and David Holtz May 30, 2024...
Learning discriminative representations for unseen person images is critical for person re-identification (ReID). Most of the current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments,...