Linear regression

Function (biology)

Context (archaeology)

Biology

Statistics

Set (abstract data type)

Data mining

Generalizability theory

Computer science

Code (set theory)

Evolutionary biology

Regression analysis

Paleontology

Regression

Mathematics

Robust regression

Artificial intelligence

Programming language

Mean squared error

Machine learning

Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, im-balanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression in modern context. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE. Code and models are available at github.com/jiawei-ren/BalancedMSE.

Balanced MSE for Imbalanced Visual Regression