Introduction to Active Learning

Active Learning was introduced by Burr Settles at the University of Wisconsin.

According to Wikipedia, Active Learning is a sub-field of Semi-Supervised Learning. Let’s understand Semi-Supervised Learning in simple terms:

“The ability to get a large number of images makes this a great candidate for semi-supervised learning.”

A very simple approach to semi-supervised learning:

1. Capture 11,000 images
2. Label 100 images and train model_1
3. Use model_1 to label the other 10,900 images
4. Train model_2 with the “labeled” 10,000 images

…results in a model_2 that does better than model_1.

This is the core idea — you use a model’s own predictions to generate pseudo-labels for unlabeled data, then retrain on that larger labeled set. Active learning takes this a step further by choosing which samples to label intelligently (e.g., the ones the model is most uncertain about), making each human annotation count more.