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 […]
The Cycle Generative adversarial Network, or CycleGAN for short, is a generator model for converting images from one domain to another domain. For example, the model can be used to translate images of horses to images of zebras, or photographs of city landscapes at night to city landscapes during the day. The benefit of the […]
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 […]
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 […]
The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products. The benefit of the Pix2Pix model is that compared to other GANs for conditional image […]
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 […]
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 […]
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 […]
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 […]
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, […]
