Example of 100 LSGAN Generated Handwritten Digits After 20 Training Epochs

How to Develop a Least Squares Generative Adversarial Network (LSGAN) in Keras

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 […]

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How to Implement a Semi-Supervised Generative Adversarial Network From Scratch

How to Implement a Semi-Supervised GAN (SGAN) From Scratch in Keras

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 […]

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How to Develop an Information Maximizing Generative Adversarial Network (InfoGAN) in Keras

How to Develop an Information Maximizing GAN (InfoGAN) in Keras

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 […]

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Example of 100 Photos of Sneakers Generated by an AC-GAN

How to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras

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, […]

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Sample of 100 Generated Images of a Handwritten Number 7 at Epoch 970 from a Wasserstein GAN.

How to Develop a Wasserstein Generative Adversarial Network (WGAN) From Scratch

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 […]

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How to Implement Wasserstein Loss for Generative Adversarial Networks

How to Implement Wasserstein Loss for Generative Adversarial Networks

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 […]

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How to Code the Generative Adversarial Network Training Algorithm and Loss Functions

How to Code the GAN Training Algorithm and Loss Functions

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 […]

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

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

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. […]

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