How to Train a Progressive Growing GAN in Keras for Synthesizing Faces

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Generative adversarial networks, or GANs, are effective at generating high-quality synthetic images. A limitation of GANs is that the are only capable of generating relatively small images, such as 64 64 pixels. The Progressive Growing GAN is an extension to the GAN training procedure that involves training a GAN to generate very small images, such as 4 4, and incrementally increasing the size of the generated images to 8 8, 16 16, until the desired output size is met. This has allowed the progressive GAN to generate photorealistic synthetic faces with 1024 1024 pixel resolution. The key innovation of the progressive growing GAN is the two-phase training procedure that involves the fading-in of new blocks to support higher-resolution images followed by fine-tuning. In this tutorial, you will discover how to implement and train a progressive growing generative adversarial network for generating celebrity faces. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code. Photo by Alessandro Caproni, some rights reserved. GANs are effective at generating crisp synthetic images, although are typically limited in the size of the images that can be generated.


An intuitive introduction to Generative Adversarial Networks (GANs)

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In the perfect equilibrium, the generator would capture the general training data distribution. As a result, the discriminator would be always unsure of whether its inputs are real or not. In the DCGAN paper, the authors describe the combination of some deep learning techniques as key for training GANs. These techniques include: (i) the all convolutional net and (ii) Batch Normalization (BN). The first emphasizes strided convolutions (instead of pooling layers) for both: increasing and decreasing feature's spatial dimensions.


Generative Adversarial Networks - Part IV

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This is the Part 4 of a short series of posts introducing and building generative adversarial networks, known as GANs. Previously: Part 1 introduced the idea of adversarial learning and we started to build the machinery of a GAN implementation. Part 2 we extended our code to learn a simple 1-dimensional pattern 1010. Part 3 we developed our code to learn to generate 2-dimensional grey-scale images that look like handwritten digits In this post we'll extend our code again to lean to generate full-colour images, learning from a dataset of celebrity face photos. The ideas should be the same, and the code shouldn't need much new added to it. Celebrity Faces A popular dataset for human faces is the celebA dataset which contains 202,599 photos, annotated with some features. A revised version was developed, called the aligned celebA dataset, where the location of the eyes is consistent across the dataset and the orientation of the heads is vertical so the mouth is below the eyes were possible. The following shows 6 samples from the dataset.


How to Get Started With Generative Adversarial Networks (7-Day Mini-Course)

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Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. Because the field is so young, it can be challenging to know how to get started, what to focus on, and how to best use the available techniques. In this crash course, you will discover how you can get started and confidently develop deep learning Generative Adversarial Networks using Python in seven days. Note: This is a big and important post. You might want to bookmark it. How to Get Started With Generative Adversarial Networks (7-Day Mini-Course) Photo by Matthias Ripp, some rights reserved.


GAN by Example using Keras on Tensorflow Backend – Towards Data Science

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Generative Adversarial Networks (GAN) is one of the most promising recent developments in Deep Learning. GAN, introduced by Ian Goodfellow in 2014, attacks the problem of unsupervised learning by training two deep networks, called Generator and Discriminator, that compete and cooperate with each other. In the course of training, both networks eventually learn how to perform their tasks.