Archive | Generative Adversarial Networks

Example of High-Quality Generated Faces Using the StyleGAN

A Gentle Introduction to StyleGAN the Style Generative Adversarial Network

Generative Adversarial Networks, or GANs for short, are effective at generating large high-quality images. Most improvement has been made to discriminator models in an effort to train more effective generator models, although less effort has been put into improving the generator models. The Style Generative Adversarial Network, or StyleGAN for short, is an extension to […]

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Synthetic Celebrity Faces at 128x128 Resolution After Tuning Generated by the Progressive Growing GAN

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

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 […]

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How to Implement Progressive Growing GAN Models in Keras

How to Implement Progressive Growing GAN Models in Keras

The progressive growing generative adversarial network is an approach for training a deep convolutional neural network model for generating synthetic images. It is an extension of the more traditional GAN architecture that involves incrementally growing the size of the generated image during training, starting with a very small image, such as a 4×4 pixels. This […]

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Plot of a Real Photo of a Horse, Translation to Zebra, and Reconstructed Photo of a Horse Using CycleGAN.

How to Develop a CycleGAN for Image-to-Image Translation with Keras

The Cycle Generative Adversarial Network, or CycleGAN, is an approach to training a deep convolutional neural network for image-to-image translation tasks. Unlike other GAN models for image translation, the CycleGAN does not require a dataset of paired images. For example, if we are interested in translating photographs of oranges to apples, we do not require […]

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A Gentle Introduction to CycleGAN

A Gentle Introduction to CycleGAN for Image Translation

Image-to-image translation involves generating a new synthetic version of a given image with a specific modification, such as translating a summer landscape to winter. Training a model for image-to-image translation typically requires a large dataset of paired examples. These datasets can be difficult and expensive to prepare, and in some cases impossible, such as photographs […]

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Plot of Satellite to Google Map Translated Images Using Pix2Pix After 100 Training Epochs

How to Develop a Pix2Pix GAN for Image-to-Image Translation

The Pix2Pix Generative Adversarial Network, or GAN, is an approach to training a deep convolutional neural network for image-to-image translation tasks. The careful configuration of architecture as a type of image-conditional GAN allows for both the generation of large images compared to prior GAN models (e.g. such as 256×256 pixels) and the capability of performing […]

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Pix2Pix GAN Translation of Product Sketches of Shoes to Photographs

A Gentle Introduction to Pix2Pix Generative Adversarial Network

Image-to-image translation is the controlled conversion of a given source image to a target image. An example might be the conversion of black and white photographs to color photographs. Image-to-image translation is a challenging problem and often requires specialized models and loss functions for a given translation task or dataset. The Pix2Pix GAN is a […]

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