How to Implement a Semi-Supervised GAN (SGAN) From Scratch in Keras

Last Updated on September 1, 2020

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 discriminator model. The discriminator model can be used as a starting point for developing a classifier model in some cases.

The semi-supervised GAN, or SGAN, model is an extension of the GAN architecture that involves the simultaneous training of a supervised discriminator, unsupervised discriminator, and a generator model. The result is both a supervised classification model that generalizes well to unseen examples and a generator model that outputs plausible examples of images from the domain.

In this tutorial, you will discover how to develop a Semi-Supervised Generative Adversarial Network from scratch.

After completing this tutorial, you will know:

• The semi-supervised GAN is an extension of the GAN architecture for training a classifier model while making use of labeled and unlabeled data.
• There are at least three approaches to implementing the supervised and unsupervised discriminator models in Keras
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