Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Balanced MSE for Imbalanced Visual Regression   Data imbalance exists ubiquitously in real-world visual regressions, e.g.,
age estimation and pose estimation, hurting the model's generalizability and
fairness. Thus, imbalanced 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. Extensive experiments on both synthetic and three real-world
benchmarks demonstrate the effectiveness of Balanced MSE.

34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
3
The categories of this paper are: physics