1. R-cnn:rich feature hierarchies for accurate object detection and semantic segmentationTechnical route: Selective search + CNN + SVMsSTEP1: Candidate Box extraction (selective search)Training: Given a picture, use the Seletive search method to extract 2000 candidate boxes from it. Due to the size of the candidate boxes, considering that the subsequent CNN requires a uniform image size, the 2000 candidate

[CNN] interpretation of Light-Head R-CNN: Balancing accuracy and speed, light-headr-cnnSpeaker: Li Zeming | researcher at the face ++ Research InstituteEdit Qu XinProduced by QbitAI | public account QbitAI
On the evening of December 20, the quantum bit eat melon club and Face ++ paper interpretation series of the third phase of the lecture, this issue in the Face (Megvii) Research Institute to interpret the

http://m.blog.csdn.net/blog/wu010555688/24487301This article has compiled a number of online Daniel's blog, detailed explanation of CNN's basic structure and core ideas, welcome to exchange.[1] Deep Learning Introduction[2] Deep Learning training Process[3] Deep learning Model: the derivation and implementation of CNN convolution neural network[4] Deep learning Model: the reverse derivation and practice of CNN

value of the C3 layer according to the local gradient δ value. (2) The weight of the C3 layer updates the value. C3 layer 6*12 A 5*5 template, we first define N=1~6,M=1~12 represents the label of the template, S,t represents the location of the parameters in the template (3) Weight update formula of C1 layer and field gradient δ value Similarly, we can also get the C1 layer weight update formula, here the m=6,n=1, and y refers to the input image the sampling layer S2 of the convolution

The first day of CNN Basics From:convolutional Neural Networks (LeNet)
neuro-Cognitive machines .The source of CNN's inspiration has been very comprehensive in many papers, and it is the great creature that found receptive Field (the sensation of wild cells). Based on this concept, a neuro-cognitive machine is proposed. Its main function is to recept part of the image information (or characteristics), and then through the hierarchical submission o

Use tensorflow to build CNN and tensorflow to build cnn
Convolutional Neural Networks Convolutional Neural Network (CNN) transfers the data of an image to CNN. The original coating is composed of RGB, And then CNN thickened the thickness and the length and width become small

Directory
Source information
Using Keras to explore the filter for convolutional networks
Visualize All Filters
Deep Dream (Nightmare)
Fool the Neural network
The revolution has not been successful, comrades still need to work hard
Source informationThis address: http://blog.keras.io/how-convolutional-neural-networks-see-the-world.htmlThis article Francois CholletThe translation of this article was first published by me in the Keras Chinese documents, in order t

This article explains in detail the network architecture and workflow of Faster R-CNN, which leads the reader to understand the principle of target detection, and the author also provides the Luminoth realization for everyone's reference.
Luminoth implementation: GITHUB.COM/TRYOLABS/LUMINOTH/TREE/MASTER/LUMINOTH/MODELS/FASTERRCNN
Last year, we decided to dig deeper into Faster r-cnn, rea

Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE Conference on Computer vision and pattern recognition. 2014.The full name of R-CNN is REGION-CNN, which can be said to be the first algorithm to successfully apply deep learning to target detection. The fast r-cnn, Faster r-

if you want to deal with different length of text, the above conclusions may not be instructive.The article [8] explores the application of CNNs in relational Mining and relational classification tasks. In addition to the word vector representation, the author also takes the relative position of the word and the word as the input value of the convolution layer. This model assumes that the position of all text elements is known, and that each input sample contains only one relationship. The lite

convolutional Neural Networks (convolution neural network, CNN) have achieved great success in the field of digital image processing, which has sparked a frenzy of deep learning in the field of natural language processing (Natural Language processing, NLP). Since 2015, papers on deep learning in the field of NLP have emerged. Although there must be a lot of arty hydrology, there are many classic application-oriented articles. In 2016, I also published

reverse the image adjustment.Figure 1-16 Deep Dream Approximate processDeep StyleDeep style is given two pictures, one is partial content, one is biased style. Then, the two images are "fused," as shown in effect 1-17Figure 1-17 Deep StyleThe deep style of the general idea of 1-18, a CNN extract the content of the picture; a CNN extract the style of the picture, the style of the image is mainly reflected i

In fact, starting from this blog post, we are really into the field of deep learning. In the field of deep learning, the proven mature algorithm, currently has deep convolutional network (DNN) and recursive Network (RNN), in the field of image recognition, video recognition, speech recognition has achieved great success, it is because of these successes, can contribute to the current deep learning of the great heat. Corresponding to this, in the field

Deep Learning paper notes (IV.) The derivation and implementation of CNN convolution neural network[Email protected]Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it seems to have not seen the same. So want to get used to some of the feeling useful papers in the knowledge points summarized, on the one hand in the process of finishing, their own understanding will be de

gradient propagation of the residual structure block). We all know how important it is to keep the gradient flow in the BP process, to prevent gradients from exploding/disappearing when training CNN, since shortcut is so effective, why not add more ? This is the core idea of Densenet: Adding a separate shortcut to each of the previous layers makes it possible to "communicate" directly between any two layers. That is, as shown in the structure:In the

convolutional Neural Network is the first multi-layered neural network structure which has been successfully trained, and has strong fault tolerance, self-learning and parallel processing ability.First, the basic principle1.CNN algorithm Ideasconvolutional neural network can be regarded as a special case of Feedforward network, which simplifies and improves Feedforward network mainly in network structure, in theory, the inverse propagation algorithm c

Ren, Shaoqing, et al. "Faster r-cnn:towards Real-time object detection with region proposal networks." Advances in neural information processing Systems. 2015.After Rcnn[1],fast Rcnn[2], this article is another masterpiece of the Ross Girshick team, the leader of the target detection community in 2015. The detection speed of simple network target is 17fps, the accuracy of Pascal VOC is 59.9%, the complex network reaches 5fps, the accuracy rate is 78.8%.The author gives the source code based on M

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural network. convolutional Neural Networks (convolutional neural network,cnn) can do that.

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn
I. Convolutional Neural Network Overview
ConvolutionalNeural Network (CNN) was originally designed to solve image recognition and other problems. CNN's current applications are not limited to images and videos, but can also be used for time series signals, for example, audio signal and text

references), refactoring is performed asynchronously by default, but pay attention to the options of synchronous refactoring.
When you experience net. py, when the reconstruction is successful, the behavior of the network is shocking. One of these actions is that even when the weight array seriously degrades, it can still refactor the pattern. My simple Degrade Weights will traverse the weight array and randomly set the weight to 0. A view of the damage degree is provided for the display of the

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