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. Developing a GAN for generating images requires both a discriminator convolutional neural network model for classifying whether a given image is real or generated and a generator model that uses inverse convolutional layers to transform an input to a full two-dimensional image of pixel values. It can be challenging to understand both how […]

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. Developing a GAN for generating images requires both a discriminator convolutional neural network model for classifying whether a given image is real or generated and a generator model that uses inverse convolutional layers to transform an input to a full two-dimensional image of pixel values. It can be challenging to understand both how […]

Last Updated on September 1, 2020 How to Use Interpolation and Vector Arithmetic to Explore the GAN Latent Space. Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The generative model in the GAN architecture learns to map points in the latent space to generated images. The latent space has no meaning other than the meaning applied to it via the generative model. Yet, the latent space has […]

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. Although GAN models are capable of generating new random plausible examples for a given dataset, there is no way to control the types of images that are generated other than trying to figure out the complex relationship between the latent space input to the generator and the generated images. The conditional generative adversarial […]

Last Updated on August 17, 2020 How to Identify Unstable Models When Training Generative Adversarial Networks. GANs are difficult to train. The reason they are difficult to train is that both the generator model and the discriminator model are trained simultaneously in a zero sum game. This means that improvements to one model come at the expense of the other model. The goal of training two models involves finding a point of equilibrium between the two competing concerns. It also […]

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 […]