Classifying Continuous Labels: A Simple Tweak to Make Regression Robust

Classifying Continuous Labels: A Simple Tweak to Make Regression Robust

Bao, Ziyu (TU Delft Electrical Engineering, Mathematics and Computer Science)

van Gemert, J.C. (mentor)
Pintea, S. (mentor)
Liem, C.C.S. (graduation committee)

Delft University of Technology

Computer Science

2022-07-04

Regression is difficult because of noise, imbalanced data sampling, missing data, etc. We propose a method by classifying the continuous regression labels to tackle regression robustness problems. We analyze if our method can help regression, given that the class information is already included in the regression labels. We start by extensively experimenting on 1D synthetic datasets and find out that classification can help regression when the data sampling is i balanced. This happens when the data are clean, noisy in inputs and noisy in outputs, but not when they are partially missing. We then validate our conclusion on the KITTI dataset by estimating 3D object orientation. We conclude that our method can help regression in real-world.

Robust Regression
Computer Vision
Loss Functions

http://resolver.tudelft.nl/uuid:21a602e7-8a2e-403d-94bc-0202e4479b1d

Student theses

master thesis

© 2022 Ziyu Bao