Deep Learning Tutorial - Convolutional Neural Networks(LENET)

CNN很多概述和要点在CS231n.Neural Networks and Deep Learning中有详细阐述,这里补充Deep Learning Tutorial中的内容.本节前提是前两节的内容,因为要用到全连接层.logistic regression层等.关于Theano:掌握共享变量,下采样,conv2d,dimshuffle的应用等. 1.卷积操作在Theano中,ConvOp是提供卷积操作的主力.ConvOp来自theano.tensor.signal.conv.conv2d,…

Coursera, Deep Learning 4, Convolutional Neural Networks - week1

CNN 主要解决 computer vision 问题,同时解决input X 维度太大的问题. Edge detection 下面演示了convolution 的概念下图的 vertical edge 看起来有点厚,但是如果图片远比6x6像素大的话,就会看到效果非常不错. 除了前面讲过的第一种filter, 还有两种 (Sobel filter, Scharr filter) 接下来会讲到 CNN 的两个重要的buiding block - padding, strided convolut…

Coursera, Deep Learning 4, Convolutional Neural Networks - week4,

Face recognition One Shot Learning 只看一次图片,就能以后识别, 传统deep learning 很难做到这个. 而且如果要加一个人到数据库里面,就要重新train model 显然不合理,所以就引出了 One Shot Learning 的概念. 怎么得出这个similarity function d(img1, img2) 呢?下面的介绍的 Siamese network.可以实现这个目标. 怎么定义object function 来满足上面的的条件呢?可以…

Coursera, Deep Learning 4, Convolutional Neural Networks - week2

Case Study (Note: 红色表示不重要) LeNet-5 起初用来识别手写数字灰度图片 AlexNet 输入的是227x227x3 的图片,输出1000 种类的结果 VGG VGG比AlexNet 结构更简单,filter 都是3x3的,max-pool 都是 2x2的. ResNets (Residual Network) 可用让很深的network 工作的很好. This really helps with the vanishing and exploding gradient…

Coursera, Deep Learning 4, Convolutional Neural Networks, week3, Object detection

学习目标 Understand the challenges of Object Localization, Object Detection and Landmark Finding Understand and implement non-max suppression Understand and implement intersection over union Understand how we label a dataset for an object detection appli…

论文笔记之：Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 本文提出了一种新的CNN 框架来处理跟踪问题.众所周知,CNN在很多视觉领域都是如鱼得水,唯独目标跟踪显得有点“慢热”,这主要是因为CNN的训练需要海量数据,纵然是在ImageNet 数据集上微调后的model 仍然不足以很好的表达要跟踪地物体,因为Tracking问题的特殊性,至于怎么特殊的,且听细细道来. 目标跟踪之所以很少被 C…

[CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural networks论文笔记

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【论文阅读】Learning Dual Convolutional Neural Networks for Low-Level Vision

论文阅读([CVPR2018]Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision) 本文针对低层视觉问题,提出了一般性的用于解决低层视觉问题的对偶卷积神经网络.作者认为,低层视觉问题,如常见的有超分辨率重建.保边滤波.图像去雾和图像去雨等,这些问题经常涉及到估计目标信号的两个成分:结构和细节.因此,文章提出DualCNN,它包含两个平行的分支来分别恢复结构和细节信息. 具体内容参见https…

卷积神经网络LeNet Convolutional Neural Networks (LeNet)

Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally, it uses the following new Theano functions and concepts: T.tanh, shared variables, basic arithme…

【DeepLearning学习笔记】Coursera课程《Neural Networks and Deep Learning》——Week2 Neural Networks Basics课堂笔记

Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week2 Neural Networks Basics 2.1 Logistic Regression as a Neutral Network 2.1.1 Binary Classification 二分类逻辑回归是一个用于二分类(binary classification)的算法.首先我们从一个问题开始说起,这里有一个二分类问题的例子,假如你有一张图片作为输入,比…

论文阅读：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

前言 CVPR2016 来自Korea的POSTECH这个团队大部分算法(例如HCF, DeepLMCF)只是用在大量数据上训练好的(pretrain)的一些网络如VGG作为特征提取器,这些做法证实利用CNN深度特征对跟踪结果有显著提升. 但是毕竟clssification 和 tracking是两个不同的课题 (predicting object class labels VS locating targets of arbitrary classes.) 所以作者设计了一个网络来做跟踪…

[转]An Intuitive Explanation of Convolutional Neural Networks

An Intuitive Explanation of Convolutional Neural Networks https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Posted on August 11, 2016 by ujjwalkarn What are Convolutional Neural Networks and why are they important? Convolutional Neural…

An Intuitive Explanation of Convolutional Neural Networks

https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ An Intuitive Explanation of Convolutional Neural Networks Posted on August 11, 2016 by ujjwalkarn What are Convolutional Neural Networks and why are they important? Convolutional Neural…

一目了然卷积神经网络 - An Intuitive Explanation of Convolutional Neural Networks

An Intuitive Explanation of Convolutional Neural Networks 原文地址:https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/comment-page-4/?unapproved=31867&moderation-hash=1ac28e426bc9919dc1a295563f9c60ae#comment-31867 一.什么是卷积神经网络.为什么卷积神经网络很重要? 卷…

Backpropagation In Convolutional Neural Networks

http://www.jefkine.com/general/2016/09/05/backpropagation-in-convolutional-neural-networks/ http://web.eecs.utk.edu/~zzhang61/docs/reports/2016.10%20-%20Derivation%20of%20Backpropagation%20in%20Convolutional%20Neural%20Network%20(CNN).pdf http://ufld…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） —— 0.Learning Goals

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a Residual network Build a deep neural network using Keras Implem…

Convolutional Neural Networks from deep learning (assignment 1 from week 1)

Convolutional Neural Networks https://www.coursera.org/learn/convolutional-neural-networks/home/welcome There are still something confuse me! working how to paste the jupyter here.................................................... Convolutional Neur…

深度卷积神经网络用于图像缩放Image Scaling using Deep Convolutional Neural Networks

This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, discuss our model and its possible applications to Flipboa…

深度学习材料：从感知机到深度网络A Deep Learning Tutorial: From Perceptrons to Deep Networks

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisition…

Image Scaling using Deep Convolutional Neural Networks

Image Scaling using Deep Convolutional Neural Networks This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, dis…

Deep learning_CNN_Review：A Survey of the Recent Architectures of Deep Convolutional Neural Networks——2019

CNN综述文章的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks 翻译综述深度卷积神经网络架构:从基本组件到结构创新目录摘要 1.引言 2.CNN基本组件 2.1 卷积层 2.2 池化层 2.3 激活函数 2.4 批次归一化 2.5 Dropout 2.6 全连接层…

《ImageNet Classification with Deep Convolutional Neural Networks》剖析

<ImageNet Classification with Deep Convolutional Neural Networks> 剖析 CNN 领域的经典之作, 作者训练了一个面向数量为 1.2 百万的高分辨率的图像数据集ImageNet, 图像的种类为1000 种的深度卷积神经网络.并在图像识别的benchmark数据集上取得了卓越的成绩. 和之间的LeNet还是有着异曲同工之妙.这里涉及到 category 种类多的因素,该网络考虑了多通道卷积操作, 卷积操作也不是 LeNet 的单通道…

AlexNet论文翻译-ImageNet Classification with Deep Convolutional Neural Networks

ImageNet Classification with Deep Convolutional Neural Networks 深度卷积神经网络的ImageNet分类 Alex Krizhevsky University of Toronto 多伦多大学 kriz@cs.utoronto.ca Ilya Sutskever University of Toronto 多伦多大学 ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toront…

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks 理解深度卷积神经网络中的有效感受野 Abstract摘要 We study characteristics of receptive ﬁelds of units in deep convolutional networks. The receptive ﬁeld size is a crucial issue in many vis…