Archive | Generative Adversarial Networks

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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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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