Label Distribution Learning Forests
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.
The authors describe a method for label distribution learning based on differentiable decision trees. The authors use differentiable sigmoid units to estimate a label distribution using leaf nodes of trees. Learning in split nodes is done via backprop. whereas for leaf nodes the authors propose a methodology based on variational bounding. The authors compare their work with relevant methods on learning label distributions and show the competitiveness of their method. 
I think this is a good paper, providing a sound methodology for learning LD. The main weakness of this paper is that it is somewhat difficult for the reader to differentiate the actual contribution of this paper with respecto to reference [20]. I would like to see an answer to this in the rebuttal phase. 
detailed comments: 
typo: The best parameters ARE determined by: 
It is not clear to me whether the way split-nodes are learned is a contribution of this paper, or if it is described in [20], please clarify
Do you have any plans of releasing the code of the LDF method with caffe? 
The numbers of trees and deep of trees is quite small, the authors make an effort to analyze the performance of their method under different parameter settings, but, the number of trees and depth is still small, what are author thoughts in much larger ensembles and tree depths? Have the authors performed experiments evaluating this setting? Please elaborate on this
For the experimental results from Section 4.1, are the results from Table 1 comparable to each other? How do you guarantee this? Please provide more details
For the problem approached in Section 4.2, can the authors please report the state of the art performance? (e.g., not only based on label distribution learning)
What is the complexity of the model?, also, can the authors report runtime?