generative adversarial network

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 yea

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

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

with digits and faces, but it created very fuzzy and vague images when using the CIFAR-10 DataSet.In order to fix this problem, Emily Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus published the paper titled "Dee P generative Image Models using Lapalacian Pyramid of adversarial Networks ". The main contribution of the paper is a type of network architect

source code is now used and borrowed the most frequency. All this must be attributed to the more robust engineering experience shared by this work than Lapgan. In other words, dcgan,deep convolutional generative adversarial Networks, the work [4], points to many of the architectural designs that are important to GAN's precarious learning style and the specific experience of the

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

This 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:　　　　Gan's original model has many shortcomings that can be improved, the first one is "model is not controllable". From the above introduction to Gan can be seen, the model with a random noise as input. Obviously, it is difficult to control the struc

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,

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

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

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 fr

0. BackgroundJunbo Zhao the "energy-based Gan" network, which treats the discriminant as an energy function without the need for a significant probability interpretation, the function can be a training loss function. The energy function is to treat the area close to the real data manifold as a low-energy region, and away from it as a high-energy region. Similar to "Probability Gan", in training, the generator will generate as much of the minimum energ

"Preface" This paper first introduces the generative model, and then focuses on the research and development of generating antagonistic network (generative adversarial networks) in the Generative model (generative Models). Accordi

Preface This article first introduces the build model and then focuses on the resulting model (generative Models), the research and development of the generative adversarial network is generated. According to Gan main thesis, gan applied paper and gan related papers, the author sorted out 45 papers in recent two yea

This blog is reproduced from a blog post, introduced Gan (generative adversarial Networks) that is the principle of generative warfare network and Gan's advantages and disadvantages of analysis and the development of GAN Network research. Here is the content. 1. Build Model

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

Formula 1 only makes a very small change in the direction of the gradient, but the model cannot be properly classified. In addition, a phenomenon was observed, using multiple classifiers of different structures to learn the same data, often mistakenly divided the same counter sample into the same class, which appears that all classifiers are disturbed by the same changes. 2. Generation vs. Network Gan 14 Goodfellow proposed