Active learning is a methodology used in machine learning that helps focus data labeling on instances in your data set that will drive the greatest value for your model. Through a variety of algorithms and processes, models are able to identify subsets of valuable data, refer these subsets to human annotators, trigger model retraining with the newly labeled information, and drive greater machine learning model accuracy with less human labeling overall.
Active learning methods provide a more cost effective path to a high performing model. Not every image or instance is worth the time or money it takes to label, but some instances are incredibly valuable once labeled well.
Active learning can be a useful tool at many stages of model development, and it is a powerful example of how models, sampling mechanisms, and human annotators can work together quickly and intelligently to produce quality results.