16 Deep Learning-based outlier detection
This chapter covers
- Exploring the general concepts of deep learning-based outlier detection
- Demonstrating some of the options available in standard libraries
- Introducing outlier detection for image data
- Discussing the state of the art in outlier detection today
Deep learning-based outlier detection techniques can be very powerful for many types of problems. For tabular data, they are typically still not as useful as the methods we’ve looked at so far in this book, but as a data scientist, you may also often work with time series, text, image, video, audio, network, or other types of data, and for many of these, deep learning-based methods can be very effective. In fact, for many types of data, including image, video, and audio, there really are no other viable options available today.
Deep-learning based outlier detection can work in a variety of ways, but all use deep neural networks in one way or another. Deep neural networks have some significant advantages as models and have proven themselves to be able to handle many types of problems that are unsolvable using other means. At the same time, they do have some costs associated with them. They tend to require a very large amount of data to train, are slower to work with, and are more difficult to tune. Still, they’ve made phenomenal progress in many fields the last several years, even with tabular data, and we will certainly see them become increasingly powerful in years to come.