Last Updated on July 12, 2019 Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success. There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes “GAN“, such as DCGAN, as opposed to a minor extension to the method. Given the vast size of the GAN literature and number of models, it can be, at the very least, confusing and frustrating as […]

Last Updated on July 12, 2019 Generative Adversarial Networks With Python Crash Course.Bring Generative Adversarial Networks to Your Project in 7 Days. 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, […]

Last Updated on January 10, 2020 The Generative Adversarial Network, or GAN for short, is an architecture for training a generative model. The architecture is comprised of two models. The generator that we are interested in, and a discriminator model that is used to assist in the training of the generator. Initially, both of the generator and discriminator models were implemented as Multilayer Perceptrons (MLP), although more recently, the models are implemented as deep convolutional neural networks. It can be […]

The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. It is an important extension to the GAN model and requires a conceptual shift away from a discriminator that predicts the probability of a generated image being “real” and toward the idea of a critic model that scores the “realness” of a […]

Last Updated on September 1, 2020 The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. The development of the WGAN has a dense mathematical motivation, although in practice requires only a few minor modifications to the established standard deep convolutional generative adversarial network, or DCGAN. In this tutorial, you will discover […]

Last Updated on September 1, 2020 Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The conditional generative adversarial network, or cGAN for short, is a type of GAN that involves the conditional generation of images by a generator model. Image generation can be conditional on a class label, if available, allowing the targeted generated of images of a given type. The Auxiliary Classifier GAN, or AC-GAN for […]

Last Updated on September 1, 2020 The Generative Adversarial Network, or GAN, is an architecture for training deep convolutional models for generating synthetic images. Although remarkably effective, the default GAN provides no control over the types of images that are generated. The Information Maximizing GAN, or InfoGAN for short, is an extension to the GAN architecture that introduces control variables that are automatically learned by the architecture and allow control over the generated image, such as style, thickness, and type […]

Last Updated on September 1, 2020 Semi-supervised learning is the challenging problem of training a classifier in a dataset that contains a small number of labeled examples and a much larger number of unlabeled examples. The Generative Adversarial Network, or GAN, is an architecture that makes effective use of large, unlabeled datasets to train an image generator model via an image discriminator model. The discriminator model can be used as a starting point for developing a classifier model in some […]

Last Updated on September 1, 2020 The Least Squares Generative Adversarial Network, or LSGAN for short, is an extension to the GAN architecture that addresses the problem of vanishing gradients and loss saturation. It is motivated by the desire to provide a signal to the generator about fake samples that are far from the discriminator model’s decision boundary for classifying them as real or fake. The further the generated images are from the decision boundary, the larger the error signal […]