Active Learning: the Theory
Active learning is still a niche approach in Machine Learning, but that is bound to change
This article was first published on April 14, 2020 on Scaleway’s official blog and is reposted here for your convenience.
For a PyTorch implementation of active learning for an image classification problem, check out part 2:
Active learning: when and why do we need it?
Active learning is still a relatively niche approach in the machine learning world, but that is bound to change. After all, active learning provides solutions to not one, but two challenging problems that have been poisoning the lives of many a data scientist. I am, of course, talking about the data: namely, its (a) quantity and (b) quality.
Let us start with the former. It is no secret that training modern machine learning (ML) models requires large quantities of training data. This is especially true for deep learning (ML carried out via deep artificial neural nets), where it is not uncommon for training sets to number in the hundreds of thousands and beyond. To make matters worse, many practical applications come in the form of supervised ML tasks: i.e…
