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The concept of Label Distribution Learning (LDL) is a technique to stabilizeclassification and regression problems with ambiguous and/or imbalanced labels.A prototypical use-case of LDL is human age estimation based on profile images.Regarding this regression problem, a so called Deep Label Distribution Learning(DLDL) method has been developed. The main idea is the joint regression of thelabel distribution and its expectation value. However, the original DLDL methoduses loss components with different mathematical motivation and, thus,different scales, which is why the use of a hyperparameter becomes necessary.In this work, we introduce a loss function for DLDL whose components arecompletely defined by Kullback-Leibler (KL) divergences and, thus, are directlycomparable to each other without the need of additional hyperparameters. Itgeneralizes the concept of DLDL with regard to further use-cases, in particularfor multi-dimensional or multi-scale distribution learning tasks.

Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning