Using less data – active learning

Part of the motivation for generative models, be they GANs or VAEs, was always that they would allow us to generate data and therefore require less data. As data is inherently sparse, especially in finance, and we never have enough of it, generative models seem as though they are the free lunch that economists warn us about. Yet even the best GAN works with no data. In this section, we will have a look at the different methods used to bootstrap models with as little data as possible. This method is also called active learning or semi-supervised learning.

Unsupervised learning uses unlabeled data to cluster data in different ways. An example is autoencoders, where images can be transformed into learned and latent vectors, which can then be clustered without the need for labels that describe the image.

Supervised learning uses data with labels. An example is the image classifier we built in Chapter 3, Utilizing Computer Vision, or most of the other models...