deep convolutional networks for large scale image recognition

deep convolutional networks for large scale image recognition

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probability estimate. Merging the two best model in Figure 3 and Figure 4 to achieve a better value, the fusion of seven model will become worse.Ten. Reference[1]. Simonyan K, Zisserman A. Very deep convolutional Networks for large-scale

Very Deep convolutional Networks for large-scale Image recognition
Reprint Please specify:http://blog.csdn.net/stdcoutzyx/article/details/39736509
This paper is in Septembe

Very Deep convolutional Networks for large-scale Image recognition reprint please specify: http://blog.csdn.net/stdcoutzyx/article/ details/39736509
This paper is in September

ILSVRC game everyone is using, why? Because doing this basically slightly, can improve the network recognition rate. Principle, it may be that the so-called different network models are somewhat complementary (complementarity). This also shows that the network is unstable ... I still haven't found the essence. In the final article, Appendix B gives the generalization of the network.The data set used in the feature extraction layer of the network. To

with the Sofamax output of multiple convolutional networks , multiple models are fused together to output results. The results are shown in table 6. 4.5 COMPARISON with the state of the ARTwith the current compare the state of the ART model. Compared with the previous 12,13 network Vgg Advantage is obvious. With googlenet comparison single model good point,7 Network fusion is inferior to googlenet. 5 Con

Very Deep convolutional Networks for large-scale Image Recognitionkaren Simonyan, Andrew ZissermanIn this work we investigate the effect of the convolutional network depth on its accura

training the detector. This method avoids the iterative calculation of convolution characteristics. When processing the test image, our method is 24-102 times faster than the R-cnn method on the VOC2007 dataset, which achieves the same or better performance. On the Imagenet large-scale Vision recognition Task Challeng

First, the main idea of this paperConsidering that the size of the input image of the traditional CNN architecture is fixed (for example: 256*256), this artificially changing the size of the input image destroys the scale and length-to-width ratio of the input image. The author considers that the input size of the con

-classification neural network (such as: BP Neural network), through the Softmax function to obtain the final output. The entire model was trained.All neurons in the two layers have a weighted connection, usually the full-attached layer at the tail end of the convolutional neural network. It is the same way that traditional neural network neurons are connected:
A detailed description of the convolutional la

evolution of deep neural networks in image recognition applications"Minibatch" You use a data point to calculate to modify the network, may be very unstable, because you this point of the lable may be wrong. At this point you may need a Minibatch method that averages the results of a batch of data and modifies it in th

output.Displays the size of the resulting output image with a 3x3 grid on the 28x28 image using different step sizes and fill methods:The following is an understanding of the convolution process with two dynamic graphs:The first is a convolution process that is effectively populated with a 3x3 grid on a 5x5 image:The second is the convolution process with the same padding on the 5x5

calculation, the result is the same.In this example, there are differences in the results, indicating that there must be random components in the system.The random parts of machine learning are usually as follows: 1. The disorderly sequence operation of the training sample; 2. Random gradient descent; 3. The model randomly assigns the initial value.In this example, there is one more: the initial input of the white noise image is randomly generated."I

Oxford University and a researcher at Google DeepMind.Vggnet explores the relationship between the depth of convolutional neural networks and their performance, by repeatedly stacking 3*3 's small convolution cores and 2*2 's largest pooled layer,Vggnet successfully constructed a convolutional neural network with deep

, deep learning can reach 99.47% recognition rate [8].While the academic community has received extensive attention, deep learning has also had a huge impact in industry. 6 months after Hinton's team won the Imagenet competition, Google and Baidu released new search engines based on image content. They followed the

Ext.: http://mp.weixin.qq.com/s?__biz=MzAwNDExMTQwNQ==mid=209152042idx=1sn= Fa0053e66cad3d2f7b107479014d4478#rd#opennewwindow1. Deep Learning development Historydeep Learning is an important breakthrough in the field of artificial intelligence in the past ten years. It has been successfully used in many fields such as speech recognition, natural language processing, computer vision,

, which is difficult to be used in real-time recognition system of industry. Therefore, Iflytek uses deep full-sequence convolutional neural network to overcome the defects of bidirectional lstm.CNN was used in speech recognition systems as early as 2012, but there was no big breakthrough. The main reason is that it us

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 norm

, which is more robust to the change of image in space.
DropoutFinally, a little mention of dropout, this is Hinton in improving neural networks by preventing co-adaptation of feature detectors[9] in the article. The method is that at the time of training, the node output of a layer of hidden layer output node is randomly selected p (such as 0.5), and the weights associated with those 0 nodes are not u

three structural ideas of local sensation field, weighted value sharing (or weight reproduction) and time or spatial sub-sampling to obtain some degree of displacement, scale and deformation invariance.Question three:If the C1 layer is reduced to 4 feature plots, the same S2 is also reduced to 4 feature plots, with C3 and S4 corresponding to 11 feature graphs, then C3 and S2 connection conditionsQuestion Fourth:Full connection:C5 to the C4 layer conv

value sharing (or weight reproduction) and time or spatial sub-sampling to obtain some degree of displacement, scale and deformation invariance.Question three:If the C1 layer is reduced to 4 feature plots, the same S2 is also reduced to 4 feature plots, with C3 and S4 corresponding to 11 feature graphs, then C3 and S2 connection conditionsQuestion Fourth:Full connection:C5 to the C4 layer convolution operation, the use of the full connection, that is

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