generative adversarial nets

, we can take advantage of a lot of the unstructured image data which is available today. After training, we can use the output or intermediate layers as feature extractors the can is used for other classifiers, Which now won ' t need as much training data to achieve good accuracy.Paper that I couldn ' t get to, but still insanely cool: Dcgans. The authors didn ' t do anything crazy. They just trained a really really large convnet, but the trick was that they had the right hyperparameters to rea

Article Link: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf This is Goodfellow's Scholar homepage, you can go to worship. Https://scholar.google.ca/citations?user=iYN86KEAAAAJ Recent recommended articles related to Gan: Unsupervised and semi-supervised Learning with categorical generative adversarial

Conditional generative adversarial NetsArXiv 2014This article is the expansion of gans, in the generation and discrimination, taking into account the additional conditions y, in order to carry out more "fierce" confrontation, so as to achieve better results. As we all know, Gans is a Minmax process:In this paper, by introducing conditional y, The objective function of optimization is changed into:The struct

Generative adversarial NetsNIPS 2014In this paper, a new framework is proposed to predict the production model through the process of confrontation, and we train two models: a production model G, which can catch the data distribution, and a discriminant model D can predict the probability of a sample from a training sample instead of G. The purpose of training G is to make D as error-making as possible, to

sample "[" Male "," US "," Internet Explorer "]" code, "male [1,0], the same as "US" corresponds to [0,1,0], "Internet Explorer" corresponds to [0,0,0,1]. The result of the complete feature digitization is: [1,0,0,1,0,0,0,0,1]. The result is that the data becomes very sparse. 2.maxout (parametric k=5)So this is why the use of maxout, the number of parameters into a K-fold increase in the reason. Originally we only need a set of parameters is enough, after the use of maxout, it is necessary to h

discriminator (x): = Tf.nn.relu (Tf.matmul (x, D_W1) + d_b1 ) = Tf.matmul (d_h1, d_w2) + d_b2 return tf.nn.sigmoid (out)"" " "" "def discriminator (x): = Tf.nn.relu (Tf.matmul (x, D_W1) + d_b1) = Tf.matmul (D_H1, d_w2) + d_b2 return outView CodeNext, modify the loss function to remove the log:"" "" "=-tf.reduce_mean (Tf.log (d_real) + tf.log (1. -=-Tf.reduce_mean (Tf.log (d_fake))"" "" "" = Tf.reduce_mean (d_real)-=-tf.reduce_mean (d_fake)View CodeAfter each gradient dro

g to minimize the value of the GAN network by using the classifier as the discriminant D. Salimans and other people although not understand the relationship between the G and the classifier, but the experiment shows in unsupervised learning, the use of feature matching method to optimize the G effect is very good, and the use of Minibatch discriminiation a little effect.The k+1 classifier here is a bit too parameterized. If you subtract a function from each output logit ( f (x) \), that is \ (L

This is Ian Goodfellow, the Great God of the 2014 years of paper, recently very hot, has not looked, left the pit. Chinese should be called Confrontation network The code is written in pylearn2 GitHub address: https://github.com/goodfeli/adversarial/ What: At the same time harmless two models: a generative model G (obtained data distribution), a differentiating model D (the predictive input is true, or is

training image is scaled to [ -1,1] to accommodate the TANH activation function used by the generator output layer; Using Relu as a nonlinear activation function; Initialization: The initializer is n (0,0.002), whereas the generator is n (0,0.02). Offsets are initialized to 0 The model is judged by "Inception score"(improved techniques for training Gans) You can see that the result of adding the PT regular item is the best, where the parameters are: (a):

Preface This article first introduces the build model, and then focuses on the generation of the generative Models in the build-up model (generative Adversarial Network) research and development. According to Gan main thesis, gan applied paper and gan related papers, the author sorted out 45 papers in recent two years, focused on combing the links and differen

Dry Goods | Existing work of generative adversarial Networks (GAN)Original 2016-02-29 small S program Yuan Daily program of the Daily What I want to share with you today is some of the work in image generation. These work are based on a large class of models, Generative adversarial Networks (GAN). From the model name

applicationsThe blogger made an open source project and collected paper and papers related to the network.Welcome to star and contribution.Https://github.com/zhangqianhui/AdversarialNetsPapersApplication to combat NN. These apps can all be found in my open source project .(1) The paper [2] uses CNN for image generation, where D is used for classification and has a good effect.(2) the thesis [3] uses the prediction of the video frame against NN, which solves the problem that other algorithms can

Deep generative Image Models using a Laplacian Pyramid of adversarial NetworksNIPS 2015　　Abstract : This paper presents a generative parametric model capable of producing high quality natural images. Our approach uses the framework of the Laplacian pyramid framework to generate images from a thick-to-thin approach using CNN cascade. At each level of the pyramid,

Photo-realistic single Image super-resolution Using a generative adversarial Network2016.10.23　　Summary :　　 contributions:Gans provides a powerful framework to produce high-quality plausible-looking natural images. This article provides a very deep ResNet architure, using the concept of Gans, to form a perceptual loss function to close human perception to do photo-realistic Sisr. 　　The mai

original text of Proposition Two is as follows: The proof of this theorem requires the use of a seemingly obvious theorem of a convex function, that is, the derivative of a function at its maximum value can be found by the secondary derivative of the upper bounds of the convex function. This theory applies to G and D, where G is invariant, and D is a convex function that has a unique optimal value, and thus can be obtained. But because I am not familiar with convex optimization theory, I do no

represent the difference of two distributions?If this problem is understood, the next step in optimizing G, to minimize the difference between this distribution is well understoodTo do some simple conversions, if we want the last step to be the largest, then the equivalent of the maximum for each x, the content of the integralHere is the given g,x,pdata (x), PG (x) is a constant, so a simple function of converting to DThe maximum value, the extremum, is the derivation to find the poleHere we de