A Visual History of Interpretation for Image Recognition

Deep learning (DL) algorithms have, over the past decade, emerged as the most competitive image recognition algorithms; however, they are by default "black box" algorithms: it is difficult to explain why they make a specific prediction. Why is that an issue? Users of ML models often want the ability to interpret which parts of the image led to the algorithm's prediction for many reasons: Motivated by these use cases, during the last decade, researchers developed many different methods to open the "black box" of deep learning, aiming to make underlying models more explainable. Some methods are specific for certain kinds of algorithms, while some are general. Some are fast, and some are slow.