convolutional neural network theory

mobilenets:efficient convolutional neural Networks for Mobile Vision applicationspaper Link:https://arxiv.org/pdf/1704.04861.pdf Abstract and prior work is a little, lazy. 1. Introductionintroduces an efficient network architecture and two hyper-parameters to build a very small, low latency (fast) model that can easily match the design requirements of mobile and

Introduction: Yesterday and everyone talked about convolutional neural network, today to bring you a paper: Pca+cnn=pcanet. Now let me take you to understand this article.Paper:pcanet:A simple deeplearning Baseline for Image classificationPaper Address: https://core.ac.uk/download/pdf/25018742.pdfArticle code: Https://github.com/Ldpe2G/PCANet1 SummaryThis Part

holds.Each neuron also holds its own output value (double). The Nnconnection and Nnweight classes store some information separately.You may wonder why the weights and connections are defined separately? According to the above principle, each connection has a weight, why not directly put them in a class?The reason: weights are often shared by the connection.In fact, the weighted value of the shared connection is in the convolutional

This tutorial uses lasagne, a tool based on Theano to quickly build a neural network:1, the realization of several neural network construction2, Discussion data augmentation method3, discuss the importance of learning "potential"4, Pre-discussion training (pre-training)The above approach will help to improve our result

convolutional Neural Networks (convolutional neural Network): A type of classifier that uses neural networks to train parameters from data, extract features, pre-determine convolution kernel size, initialize randomly, and after fe

I. Documentation names and authorsconvolutional neural Networks at Constrained time COST,CVPR two. Reading timeJune 30, 2015Three. Purpose of the documentThe author hopes to improve the accuracy of CNN by modifying the model depth and the parameters of the convolution template, while maintaining the computational complexity. Through a lot of experiments, the author finds the importance of different parameters in the

useful when combined with a number of different random subsets of other neurons. The first two fully connected layers use dropout. Without dropout, our network would show a lot of overfitting. The dropout increases the number of iterations required for convergence by roughly one-fold.4. Image preprocessing① size NormalizationTo 256x256 all the pictures to the size of the scale, as for why not directly normalized to 224 (227), please refer to the abov

Adit DeshpandeCS undergrad at UCLA (' 19)Blog Abouta Beginner ' s Guide to Understanding convolutional neural Networks Part 2IntroductionLink to Part 1In this post, we'll go to a lot more of the specifics of Convnets. Disclaimer: Now, I did realize that some of these topics is quite complex and could be made in whole posts by themselves. In a effort to remain concise yet retain comprehensiveness, I'll provi

are several forms of activation functions in convolutional neural networks: A is a fixed parameter in the formula. In the formula, each batch training sample is randomly sampled from the distribution of the mean value, which is taken in the test. From the above convolution neural network, we can see that gradient

Wholeimage to do training, do not carry patchwise sampling. The experiment proves that the direct use of the whole map has been very effectiveand efficient.A full 0 initialization is done for the Classscore convolution layer. Stochastic initialization has no advantage in performance and convergence."Experimental Design"1, compare 3 kinds of cnn:alexnet with good performance, VGG16, googlenet experiment, choose VGG162, compare fcn-32s-fixed, Fcn-32s, Fcn-16s, fcn-8s, prove the best dense predict

of the word vector effect is also possible.Channel (Channels): An image can take advantage of (R, G, B) as a different channel, while the input channel of the text is usually a different way of embedding (such as Word2vec or glove), In practice, the use of static word vectors and fine-tunning-word vectors as different channel methods are also used.One dimensional convolution (conv-1d): The image is a two-dimensional data, the word vector expression of the text is one-dimensional data, so in tex

a summary of neural networks found that now every day to see things have a new understanding, but also to the knowledge of the past. Before listening to some of Zhang Yuhong's lessons, today I went to see some of his in-depth study series in the cloud-dwelling community, it introduces the development of neural network history, the teacher is very humorous,