A step towards understanding why classification helps regression

Conference paper (2023)

Authors

S. Pintea Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Y. Lin Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Jouke Dijkstra Leiden University Medical Center
J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2023 S. Pintea, Y. Lin, Jouke Dijkstra, J.C. van Gemert
DOI
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https://doi.org/10.1109/ICCV51070.2023.01828
More Info
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in practice and when it is beneficial. To do so, we start from precisely controlled dataset variations and data samplings and find that the effect of adding a classification loss is the most pronounced for regression with imbalanced data. We explain these empirical findings by formalizing the relation between the balanced and imbalanced regression losses. Finally, we show that our findings hold on two real imbalanced image datasets for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on the problem of imbalanced video progress prediction (Breakfast). Our main takeaway is: for a regression task, if the data sampling is imbalanced, then add a classification loss.