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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Example of 100 Generated items of Clothing using a Conditional GAN.

How to Develop a Conditional GAN (cGAN) From Scratch

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 […]

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Plot of Randomly Generated Faces Using the Loaded GAN Model

How to Explore the GAN Latent Space When Generating Faces

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 […]

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Example of 100 GAN Generated CIFAR-10 Small Object Photographs

How to Develop a GAN to Generate CIFAR10 Small Color Photographs

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 […]

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Plot of 100 GAN Generated MNIST Figures After 100 Epochs

How to Develop a GAN for Generating MNIST Handwritten Digits

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 […]

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