  Regression via classification (RvC) is a common method used for regression
problems in deep learning, where the target variable belongs to a set of
continuous values. By discretizing the target into a set of non-overlapping
classes, it has been shown that training a classifier can improve neural
network accuracy compared to using a standard regression approach. However, it
is not clear how the set of discrete classes should be chosen and how it
affects the overall solution. In this work, we propose that using several
discrete data representations simultaneously can improve neural network
learning compared to a single representation. Our approach is end-to-end
differentiable and can be added as a simple extension to conventional learning
methods, such as deep neural networks. We test our method on three challenging
tasks and show that our method reduces the prediction error compared to a
baseline RvC approach while maintaining a similar model complexity.
