一 摘要 在本文中,我们提出了一个非常简单的图像分类深度学习框架,它主要依赖几个基本的数据处理方法:1)级联主成分分析(PCA);2)二值化哈希编码;3)分块直方图.在所提出的框架中,首先通过PCA方法学习多层滤波器核,然后使用二值化哈希编码以及分块直方图特征来进行下采样和编码.因此,该框架称为PCANet,并且很容易设计与学习.为了进行比较并且更好的理解,我们还介绍和研究了PCANet的两个类似的框架:RandNet和LDANet.它们与PCANet有相同的拓扑结构,但RandNet的滤波器核…

1 上采样与下采样 缩小图像(或称为下采样(subsampled)或降采样(downsampled))的主要目的有两个: 使得图像符合显示区域的大小 生成对应图像的缩略图 下采样原理:对于一幅图像I尺寸为M*N,对其进行s倍下采样,即得到(M/s)*(N/s)尺寸的得分辨率图像,当然s应该是M和N的公约数才行,如果考虑的是矩阵形式的图像,就是把原始图像s*s窗口内的图像变成一个像素,这个像素点的值就是窗口内所有像素的均值. 放大图像(或称为上采样(upsampling)或图像插值(interpo…

论文题目<Deep Learning for Hyperspectral Image Classification: An Overview> 论文作者:Shutao Li, Weiwei Song, Leyuan Fang,Yushi Chen, Pedram Ghamisi,Jón Atli Benediktsson 论文发表年份:2019 发表期刊:IEEE Transactions on Geoscience and Remote Sensing 一.高光谱简述 高光谱成像是一项重要的…

识别葡萄的一种虫害,比较了传统SIFT和深度学习分类,最后还做了目标检测 分类用的 MobileNet,目标检测 RetinaNet MobileNet 是将传统深度可分离卷积分成了两步,深度卷积和逐点卷积,性能基本不受影响情况下,降低计算量和参数量 RetinaNet 主要提出 focal loss,用于处理目标检测时正负样本不均衡问题,FL的作用如下 网络结构就是 FPN + sub-network + FL FPN是图像金字塔网络,对不同scale的图像进行融合和预测,主要用于目标检测,原…

(原文地址:http://www.cnblogs.com/idealer3d/p/LearningRaphaelJSVectorGraphics.html) 前面3篇博文里面,我们讲解了一本叫做<Instant RaphaelJS Starter>的书,那本书里我们将RaphaelJS里面的基本内容都进行了比较详细的讲解.但是,坦白的说,那本书通俗易懂却太过浅显.现在我们下一本叫做<Learning Raphael JS Vector Graphics>的书,算是上一本的进阶.当然…

In this post, I review the literature on semantic segmentation. Most research on semantic segmentation use natural/real world image datasets. Although the results are not directly applicable to medical images, I review these papers because research o…

7.27 暑假开始后,稍有时间,“搞完”金融项目,便开始跑跑 Deep Learning的程序 Hinton 在Nature上文章的代码 跑了3天 也没跑完 后来Debug 把batch 从200改到20 勉强跑出结果 后来开始看 文章等  感觉晕晕乎乎 又翻到:Deep Learning Tutorials 装Theano等,但是python 代码 Debug真是好生恶心 再后来翻到 UFLDL,看着有Exercise 便做了起来. 用了5天刷了9个Exercises. 大概年后吧,在微博上看…

转自:https://github.com/terryum/awesome-deep-learning-papers Awesome - Most Cited Deep Learning Papers A curated list of the most cited deep learning papers (since 2010) I believe that there exist classic deep learning papers which are worth reading re…

转自:https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms There are a number of dimensions you can look at to give you a sense of what will be a reasonable algorithm to start with, namely: Number of training examples Dim…

Applied Deep Learning Resources A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings. Including trained models and simple methods that can be used out of the box. Mainly focusing on Convoluti…

Introduction Deep learning is a recent trend in machine learning that models highly non-linear representations of data. In the past years, deep learning has gained a tremendous momentum and prevalence for a variety of applications (Wikipedia 2016a).…

What's the most effective way to get started with deep learning?       29 Answers     Yoshua Bengio, My lab has been one of the three that started the deep learning approach, back in 2006, along with Hinton's... Answered Jan 20, 2016   Originally Ans…

Ensemble Methods for Deep Learning Neural Networks to Reduce Variance and Improve Performance 2018-12-19 13:02:45 This blog is copied from: https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/ Deep learning neural ne…

如何提高深度学习性能 20 Tips, Tricks and Techniques That You Can Use ToFight Overfitting and Get Better Generalization How can you get better performance from your deep learning model? It is one of the most common questions I get asked. It might be asked as: H…

26 THINGS I LEARNED IN THE DEEP LEARNING SUMMER SCHOOL In the beginning of August I got the chance to attend the Deep Learning Summer School in Montreal. It consisted of 10 days of talks from some of the most well-known neural network researchers. Du…

In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks. That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow…

Open Data for Deep Learning Here you’ll find an organized list of interesting, high-quality datasets for machine learning research. We welcome your contributions for curating this list! You can find other lists of such datasets on Wikipedia, for exam…

AlexNet / VGG-F network visualized by mNeuron. Project 6: Deep LearningIntroduction to Computer Vision Brief Due date: Tuesday, December 6th, 11:55pm Project materials including starter code, training and testing data, and html writeup template: proj…

A Survey of Visual Attention Mechanisms in Deep Learning 2019-12-11 15:51:59 Source: Deep Learning on Medium Visual Glimpses and Reinforcement Learning The first paper we will look at is from Google’s DeepMind team: “ Recurrent Models of Visual Atten…

 https://study.163.com/provider/400000000398149/index.htm?share=2&shareId=400000000398149( 欢迎关注博主主页,学习python视频资源,还有大量免费python经典文章)   https://timdettmers.com/2018/12/16/deep-learning-hardware-guide/ 深度学习的完整硬件指南 深度学习是计算密集型的,因此您需要具有多个内核的快速CPU,对吧?或者购买快速C…

理论知识:Deep learning:四十一(Dropout简单理解).深度学习(二十二)Dropout浅层理解与实现.“Improving neural networks by preventing co-adaptation of feature detectors” 感觉没什么好说的了,该说的在引用的这两篇博客里已经说得很清楚了,直接做试验吧 注意: 1.在模型的测试阶段,使用”mean network(均值网络)”来得到隐含层的输出,其实就是在网络前向传播到输出层前时隐含层节点的输出值都…

Awesome Deep Learning  Table of Contents Free Online Books Courses Videos and Lectures Papers Tutorials Researchers WebSites Datasets Frameworks Miscellaneous Contributing Free Online Books Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Cou…

  Deep Learning Research Review Week 2: Reinforcement Learning 转载自: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning This is the 2nd installment of a new series called Deep Learning Resea…

前言: 本文主要是bengio的deep learning tutorial教程主页中最后一个sample:rnn-rbm in polyphonic music. 即用RNN-RBM来model复调音乐,训练过程中采用的是midi格式的音频文件,接着用建好的model来产生复调音乐.对音乐建模的难点在与每首乐曲中帧间是高度时间相关的(这样样本的维度会很高),用普通的网络模型是不能搞定的(普通设计网络模型没有考虑时间维度,图模型中的HMM有这方面的能力),这种情况下可以采用RNN来处理,这里的R…

原文转载:http://licstar.net/archives/328 Deep Learning 算法已经在图像和音频领域取得了惊人的成果,但是在 NLP 领域中尚未见到如此激动人心的结果.关于这个原因,引一条我比较赞同的微博. @王威廉:Steve Renals算了一下icassp录取文章题目中包含deep learning的数量,发现有44篇,而naacl则有0篇.有一种说法是,语言(词.句子.篇章等)属于人类认知过程中产生的高层认知抽象实体,而语音和图像属于较为底层的原始输入信号,所以…

Deep Learning in a Nutshell: History and Training This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an…

1前言 本人写技术博客的目的,其实是感觉好多东西,很长一段时间不动就会忘记了,为了加深学习记忆以及方便以后可能忘记后能很快回忆起自己曾经学过的东西. 首先,在网上找了一些资料,看见介绍说UFLDL很不错,很适合从基础开始学习,Adrew Ng大牛写得一点都不装B,感觉非常好,另外对我们英语不好的人来说非常感谢,此教程的那些翻译者们!如余凯等.因为我先看了一些深度学习的文章,但是感觉理解得不够,一般要自己编程或者至少要看懂别人的程序才能理解深刻,所以我根据该教程的练习,一步一步做起,当然我也参考了…

HOME ABOUT CONTACT SUBSCRIBE VIA RSS   DEEP LEARNING FOR ENTERPRISE Distributed Deep Learning, Part 1: An Introduction to Distributed Training of Neural Networks Oct 3, 2016 3:00:00 AM / by Alex Black and Vyacheslav Kokorin Tweet inShare27   This pos…

FastML Machine learning made easy RSS Home Contents Popular Links Backgrounds About Deep learning architecture diagrams 2016-09-30 Like a wild stream after a wet season in African savanna diverges into many smaller streams forming lakes and puddles,…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About Resume Deep Learning Research Review Week 1: Generative Adversarial Nets Starting this week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summa…