Generative adversarial networks(GANs) are a set of deep neural network models, introduced by Ian Goodfellow in 2014, used to generate synthetic data. GANs have been used in a wide variety of tasks including improving astronomical images, up-scaling resolution of old video games, and the most well known application, ‘deepfakes’ which involve human image synthesis. In this post, I will walk through some interesting data sets that can be used to train GAN models. This catalogue of data can serve as a starting point for those interested in getting started on building GAN models.

Let’s get started!

To start, let’s briefly go over the concepts behind GAN models. A GAN is composed of two competing neural networks, a generator and a discriminator. The generator is a modified convolutional neural network that learns to produce synthetic data from noise. The discriminator is a convolutional neural network that learns to distinguish between fake and real data. As model training proceeds the discriminator gets better at distinguishing between real and fake data and the generator gets better at generating realistic data.

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