The internet is abundant with videos of algorithm turning horses to zebras or fake Obama giving a talk.

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GANs were first proposed by Goodfellow et. I had been postponing looking into GANs until recently. I have spent the past few days understanding GAN and in this post, I attempt to explain it. Let me just recap neural networks before I go into GANs. Consider the neural network to be a function. It gets some input and produces some output. The main thing here is that it is called a Universal Function Approximator.

That is, it can approximate any function. If we have the inputs x as 1,2,3,4,5,6,7,8,9,10, they would become 13, 23, 33, 43, 53, 63, 73, 83, 93, As you can see, the function transforms the input from one value to another value. Apart from changing one value to another, a function can also change the feature space of these values. If we plot this, we get a plane. We have now gone from one-dimensional input to two-dimensional output. For most of the tasks, we have used neural networks as a discriminative model.

This model does not care about how the data was generated. Its task is to just classify the input. A generative modelon the other hand, tries to model how the data was generated. If it is told that the image is of a cat, then it tries to generate the original image based on the label.

If the task is to generate images of faces, the neural network needs to learn the function that maps some input to output as faces. Something like this. Say, we had an input size of i. The network has to not only learn to transform dimentional input to an image but also has to increase the feature space. Now the question becomes how do we train such a model. This is where the adversarial part comes in. GANs consists of two networks, one to produce the image from a given input and the other to see the image and tell whether it is from the real data or generated data.

Taking the example given in the paper by Goodfellow et.Code available on Github. A GAN consists of two components; a generator which converts random noise into images and a discriminator which tries to distinguish between generated and real images. To train the model we let the discriminator and generator play a game against each other.

We first show the discriminator a mixed batch of real images from our training set and of fake images generated by the generator.

We then simultaneously optimize the discriminator to answer NO to fake images and YES to real images and optimize the generator to fool the discriminator into believing that the fake images were real. This corresponds to minimizing the classification error wrt. With careful optimization both generator and discriminator will improve and the generator will eventually start generating convincing images. We implement the generator and discriminator as convnets and train them with stochastic gradient descent.

The discriminator is a standard convnet with consecutive blocks of convolution, ReLU activation, max-pooling and dropout. This is a pretty standard architecture. The generator goes in the opposite direction.

We start with a small image which is upsampled and convolved repeatedly:. To generate an image we feed the generator with noise distributed N 0,1. After successful training, the output should be meaningful images! In principle, the GAN optimization game is simple. We use binary cross entropy to optimize the parameters in the discriminator. Afterwards we use binary cross entropy to optimize the generator to fool the discriminator.

That said, you often find yourself left with not very convincing outputs from generator:. A couple of tricks are necessary for to facilitate training: First off, we need to make sure that that neither the generator nor the discriminator becomes too strong compared to the other. Conversely, if we allow the generator to win, it is usually exploiting a non-meaningful weakness in the discriminator e.

Below we plot these quantities during trained for three separate networks. In panel A we have made the discriminator too powerful by adding batch normalization layers. The training never converges because the sigmoid saturates resulting in a poor error signal for backpropagation.

To alleviate the problem, we monitor how good the discriminator is at classifying real and fake images and how good the generator is at fooling the discriminator. If one of the networks is too good, we skip updating its parameters according to the following rules.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This project was bootstrapped with Create React App. Runs the app in the development mode. Launches the test runner in the interactive watch mode. See the section about running tests for more information.

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Available Scripts In the project directory, you can run: npm start Runs the app in the development mode. The page will reload if you make edits.Our original project focus was creating a pipeline for photo restoration of portrait images. Many damaged photos are available online, and current photo restoration solutions either provide unsatisfactory results, or require an advanced understanding of image editing software.

Shortly after beginning the project, we narrowed our scope to what we consider the most interesting subproblem of photo restoration: facial image completion. The image completion problem we attempted to solve was as follows, given an image of a face with a rectangular section of the image set to be white, fill in the missing pixels.

There are previous works that attempt to solve similar problems. Graph Laplace for Occluded Face Completion and Recognition and Partially occluded face completion and recognition both leverage a large image database to find similar faces to use to complete the missing patch, but results are only shown for low resolution grey scale images. Image Denoising and Inpainting with Deep Neural Networks uses deep networks pre-trained with denoising autoencoders for image inpainting, but do no show completion of large missing patches.

In our approach we use a generator, a collection of convolution and deconvolution layers, to reconstruct the original unmasked image. The generator takes the form of a fully convolutional autoencoder. A discriminator is also trained using the output of the generator.

The discriminator's output is used together with a reconstruction cost to update the weights of the generator. We use the CelebA Dataset to train our model. The dataset consists of aroundimages including over 10, unique identities. We reserve images as a test set, and images as a validation set. For each image, we set a randomly sized patch to be white. The patch's X,Y coordinates are selected randomly. DCGAN Guidelines The architecture adheres to the following guidelines suggested in Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks which seemed to help make training stable: Replace any pooling layers with strided convolutions discriminator and fractional-strided convolutions generator.

Use batchnorm in both the generator and the discriminator. Remove fully connected hidden layers for deeper architectures.

Use ReLU activation in generator for all layers except for the output, which uses Tanh. Use LeakyReLU activation in the discriminator for all layers. Around 10k iterations, the encoding cost reaches a local minimum. A possible explanation is that as the MSE encoding cost converges on a lower bound, the autoencoder is no longer able to fool the discriminator by updating with a loss function that so heavily favors the already converged MSE. A possible solution would be to use a dynamic learning mechanism to adjust the cost function for the autoencoder.

As training progresses, the parameter X should be updated to weight MSE loss less once MSE cost has converged because learning from MSE no longer seems to be "efficient" in the sense of fooling the discriminator.

To help better understand how the autoencoder and discriminator work together, we ran several control experiments based on our initial setup. Without Discriminator without discriminator mse loss We see that the discriminator does help with reconstruction even though it's objective is to discriminate between real face data and generated face data.

This leads the generator to learn more from the discriminator and reach an equilibrium quickly, but leads to slightly worse reconstruction of the original input. We believe that generated and real image discriminator loss reaching the equilibrium point too early leads the discriminator and autoencoder to stop learning from one another. This is because both sides already believe they are doing a good job based on their objective.

As mentioned before, we believe that adding a way to actively adjust the X parameter may lead to more stable training. The original implementation provided by Enhancing Images Using Deep Convolutional Generative Adversarial Networks DCGANs performs the additional step of max pooling on the reconstructed and target image before calculating the mean squared error. Our base implementation also uses this method. The reason described for including max pooling is that it counteracts the blurred look that is often present when using denoising autoencoders.

We trained an additional model to test whether pooling was effectively preventing blur.Most of the created digits looked nice. There was only one drawback — some of the created images looked a bit cloudy. The VAE was trained with the mean squared error loss function.

## Image Generation with DCGAN

And does it really matter if the edge of a character starts a few pixels more to the left or right? In this article, we will see how we can train a network that does not depend on the mean squared error or any related loss function — instead, it will learn all by itself what a real image should look like. I got inspired to complete this project by an awesome article written by Rowel Atienza in late March, where he taught us how to apply the same technique in keras.

Afterwards, we will apply our knowledge in an even cooler project — with only a few minor tweaks, our network will learn how to draw semi- realistic human faces! In this case, these are:. The basic idea is that both network parts compete with each other. Similarly, when the generator becomes better, the discriminator has to become better also, else it will lose the ability to distinguish fake from real content.

One of these projects is the generation of MNIST characters, another is the generation of human faces. If you would like to see the whole code of this tutorial, go to my github account and take a look at the code for MNIST and face generation.

We set our batch size to be Our generator will take noise as input.

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The number of these inputs is being set to Batch normalization considerably improved the training of this network. For tensorflow to apply batch normalization, we need to let it know whether we are in training mode. I firstly tried to apply standard ReLUs to this network, but this lead to the well-known dying ReLU problemand I received generated images that looked like artwork by Kazimir Malevich — I just got black squares.

Now, we can define the discriminator. It looks similar to the encoder part of our VAE. As input, it takes real or fake MNIST digits 28 x 28 pixel grayscale images and applies a series of convolutions. Finally, we use a sigmoid to make sure our output can be interpreted as the probability the input image is a real MNIST character. The generator — just like the decoder part in our VAE — takes noise and tries to learn how to transform this noise into digits. To this end, it applies several transpose convolutions.

After applying batch normalization layers, learning improved considerably. Also, I firstly had a much larger dense layer accepting the generator input.

This led to the generator creating the same output always, no matter what the input noise was, e. On the other hand, not using a dense layer at all led to the generator not learning anything meaningful after many iterations. Tuning the generator honestly took quite some effort! Now, we wire both parts together, like we did for the encoder and the decoder of our VAE in the last tutorial. However, we have to create two discriminator objects:. We need both instances for computing two types of losses:.

To accomplish this, we use the binary cross entropy function defined earlier. The generator tries to achieve the opposite goal, it tries to make the discriminator assign high values to fake images.I studied and learned about it recently.

I think it would be nice if share my experiment to everyone. GAN is mostly about generating something. In this article I want to share about the experiment on generating anime character faces.

I also see that the faces that are generated follow a statistic distribution, which is really awesome. This article will be focused on the tutorial how to do GAN with each steps explained with source code. It will be targeted for anyone who is interested in AI, especially who want to practice on using Deep Learning.

It also targets everyone who want to learn how to do GAN for the first time.

For now, I will give the python notebook and Colaboratory link in the repository.

### Generative Adversarial Denoising Autoencoder for Face Completion

Image 0 is one of the generated anime character faces that we will create by using the picture formed by the model. The first and second picture from the left is generated with GAN. The third is the addition of the first and second faces You can call it a fusion of the first and second faces.

Introduced by Ian Goodfellow et al. In Computer Vision. There are many researchers out there researching and improving it.

There are also some research on the music domain on using GAN. My previous article that shows about generating music can also be done by using GAN. There are many variant type of GAN developed by the researchers out there. One of the newest by the time I write this article is HoloGAN that can generate 3D representation from natural images. If you look at how it can do, it is actually amazing.

Every GAN out there have two agents as its learner, discriminator and generator We will dive into these terms later. It is one of the popular GAN neural network. We will build a different architecture from the proposed architecture in their paper. Although different, it still yield some good results.

One of the interesting things about GAN is that it will build its latent variables a 1-D vector of any length which can be linear algebra operated.

### Deep Convolutional Generative Adversarial Network

The example on Image 0 is one of the example. Then, it yield the third face. It also yield some interesting data distribution.A GAN takes a different approach to learning than other types of neural networks. If both are functioning at high levels, the result is images that are seemingly identical real-life photos. Generative Adversarial Networks have had a huge success since they were introduced in by Ian J. It has been noticed most of the mainstream neural nets can be easily fooled into misclassifying things by adding only a small amount of noise into the original data.

Surprisingly, the model after adding noise has higher confidence in the wrong prediction than when it predicted correctly. The reason for such an adversary is that most machine learning models learn from a limited amount of data, which is a huge drawback, as it is prone to overfitting.

Also, the mapping between the input and the output is almost linear. Although it may seem that the boundaries of separation between the various classes are linear, in reality, they are composed of linearities and even a small change in a point in the feature space might lead to misclassification of data. GANs learn a probability distribution of a dataset by pitting two neural networks against each other.

It does so in the hopes that they, too, will be deemed authentic, even though they are fake. The fake image is generated from a dimensional noise uniform distribution between What Generator does is Density Estimation, from the noise to real data, and feed it to Discriminator to fool it.

Here, the discriminator would want to maximize the log probability of predicting zero, indicating the data is fake. The generator, on the other hand, tries to minimize the log probability of the discriminator being correct. The solution to this problem is an equilibrium point of the game, which is a saddle point of the discriminator loss. Now the question is why this is a minimax function? They both learn together by alternating gradient descent.

While the idea of GAN is simple in theory, it is very difficult to build a model that works. In GAN, there are two deep networks coupled together making backpropagation of gradients twice as challenging. Convolutional networks help in finding deep correlation within an image, that is they look for spatial correlation. This dataset is great for training and testing models for face detection, particularly for recognizing facial attributes such as finding people with brown hair, are smiling, or wearing glasses.

Images cover large pose variations, background clutter, diverse people, supported by a large number of images and rich annotations. The generator goes the other way: It is the artist who is trying to fool the discriminator.

This network consists of 8 convolutional layers.