Deep Ordinal Regression with Label Diversity
This work addresses a specific issue in deep learning regression for practitioners, but it is incremental as it builds on existing RvC methods.
The paper tackled the problem of improving regression via classification (RvC) by proposing the use of multiple discrete data representations simultaneously, which reduced prediction error compared to a baseline RvC approach while maintaining similar model complexity.
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.