A step towards understanding why classification helps regression

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
EditorsCristina Ceballos
Place of PublicationPiscataway
Number of pages10
ISBN (Electronic)979-8-3503-0718-4
ISBN (Print)979-8-3503-0719-1
Publication statusPublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision (ICCV) - Paris, France
Duration: 1 Oct 20236 Oct 2023

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


Dive into the research topics of 'A step towards understanding why classification helps regression'. Together they form a unique fingerprint.

Cite this