Active Learning for classification models

In the last few years, deep learning models achieved groundbreaking results on several computer vision tasks. Yet these models rely on vast amounts of carefully labeled images. The collection of the dataset images is substantially cheaper compared to the price of high-quality annotations. Alternatively, one can collect images from the internet with different tags and use these tags as labels, or crowdsource the annotation process, resulting in much cheaper yet noisier annotations. Active learning algorithms help deep learning engineers select a subset of images from a large unlabeled pool of data in such a way, that obtaining annotations of those images will result in a maximal increase of model accuracy.