Outlier Detection using Generative Adversarial Networks

Outlier detection techniques are widely used for solving tasks such as fraud detection, anomalous tissue detection in medical imaging data, or improving voice recognition models. By cleaning training datasets of outlying data points, unsupervised machine learning algorithms also benefit from a significant performance increase.

This report investigates the Generative Adversarial Network model, a technique for training generative models to learn the underlying probability distribution of an arbitrary training dataset through discirminative means. The work carried on in this project shows how the latent space of a Generative Adversarial Network model can be exploited for the task of outlier detection in Computer Vision tasks, as well as providing an extension that allows a Generative Adversarial Network to detect outliers in generic, high-dimensional datasets.