convolutional neural network example

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

weight, the input node after the activation function f, get output. Where functions are called "Activation functions."Here, we use the sigmoid function as the activation function f (x):Its function image is shown below: It takes a range of [0, 1]. So, for a neuron, the whole process is to enter data into the neuron, then activate the function, make some kind of conversion to the data, and finally get an output. Neural

absrtact : This paper will analyze the basic principle of deep neural network to recognize graphic images in detail. For convolutional neural Networks, this paper will discuss in detail the principle and function of each layer in the network in the image recognition, such as

Wang, Min, Baoyuan Liu, and Hassan Foroosh. "Factorized convolutional neural Networks." ArXiv preprint (2016). This paper focuses on the optimization of the convolution layer in the deep network, which has three unique features:-Can be trained directly . You do not need to train the original model first, then use the sparse, compressed bits and so on to compress.

distribution or probability model of the predicted results and samples of the degree of fit. The lower the confusion, the better the degree of fit. The calculation of the confusion histogram is shown in Flow 2:Figure 2 The construction process of the confusion histogram. (a) Sampled-area instances of the sensed region, (b) the excitation of the neurons in each area of the perceptual region, the color mapping of the excitation value, (c) the excitation of a series of neurons in the layer is tran

of the "object" in the "the position with the maximum score Use a cost function this can explicitly model multiple objects present in the image. Because there may be many objects in the graph, the multi-class classification loss is not applicable. The author sees this task as multiple two classification questions, loss function and classification score as followsTrainingMuti-scale TestExperimentClassification MAP on VOC test: +3.1% compared with [56] MAP on VOC test: +7.

. We use the cublas. lib and curand. Lib libraries. One is matrix calculation and the other is random number generation. I applied for all the memory I needed at one time. After the program started running, there was no data exchange between the CPU and GPU. This proved to be very effective. The program performance is about dozens of times faster than the original C language version (if the network is relatively large, it can reach a speed-up ratio of

Minimalist notes Deepid-net:object detection with deformable part Based convolutional Neural Networks Paper Address Http://www.ee.cuhk.edu.hk/~xgwang/papers/ouyangZWpami16.pdf This is the CUHK Wang Xiaogang group 2017 years of a tpami, the first hair in the CVPR2015, increased after the experiment to cast the journal, so the contrast experiment are some alexnet,googlenet and other early

alexnet Summary Notes Thesis: "Imagenet classification with Deep convolutional neural" 1 Network Structure The network uses the logic regression objective function to obtain the parameter optimization, this network structure as shown in Figure 1, a total of 8 layer

ImageNet classification with deep convolutional neural Networks reading notes(2013-07-06 22:16:36) reprint Tags: deep_learning imagenet Hinton Category: machine learning (after deciding to read a paper each time, the notes are recorded on the blog.) )This article, published in NIPS2012, is Hinton and his students are using deep learning in response to doubts about deep learn

used in each convolutional layer are the same (very important properties) So that the content on the image is not affected by the position, because the filter on a graph is the same, regardless of where "1" appears in the figure, the result of the filter is the same. Greatly reduce the parameters of the neural network Exampl