Abstract: Classification and regression are two fundamental tasks of machine learning. The choice between the two usually depends on the categorical or continuous nature of the target output. Curiously, in computer vision, specifically with deep learning, regression-type problems such as depth estimation, age estimation, crowd-counting and pose estimation, often yield better performance when formulated as a classification task. The phenomenon of classification outperforming regression on inherently continuous estimation tasks naturally begs the question – why? In this talk, I will highlight some possible causes based on some task-specific investigations for pose estimation and crowd-counting related to label accuracy and strength of supervision. I will then introduce a more general comparison between classification and regression from a learning point of view. Our findings suggest that the key difference lies in the learned feature spaces from the different losses used in classification versus regression.