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…

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…

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…

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).…