Content loss and conditional space relationship in conditional generative adversarial networks

Context-conditional Generative Adversarial Networks

We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as...

Bidirectional Conditional Generative Adversarial Networks

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples (x) conditioned on both latent variables (z) and known auxiliary information (c). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles z and c in the generation process and provides an encoder that learns inverse mappings from x to both z and c, trained jointly with the...

Conditional Generative Adversarial Nets

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustr...

Towards Recovery of Conditional Vectors from Conditional Generative Adversarial Networks

A conditional Generative Adversarial Network allows for generating samples conditioned on certain external information. Being able to recover latent and conditional vectors from a conditional GAN can be potentially valuable in various applications, ranging from image manipulation for entertaining purposes to diagnosis of the neural networks for security purposes. In this work, we show that it i...

Sequence synthesis with Conditional Generative Adversarial Recurrent Neural Networks