Label Distribution Learning Forests
This addresses the limitations of current LDL methods for applications requiring distributional label predictions, though it appears incremental as it builds on existing differentiable tree frameworks.
The paper tackles the problem of label distribution learning (LDL) by proposing LDLFs, a novel algorithm based on differentiable decision trees that can model any general form of label distributions and integrate with representation learning, achieving significant improvements over state-of-the-art methods on several tasks.
Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.g., to learn deep features in an end-to-end manner. This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions. 2) The learning of differentiable decision trees can be combined with representation learning. We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding. The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.