If well learned, style relations are useful knowledge for improving the performance, e.g., increasing the probability of Soul Music if we find that it is heavily linked with two high probability labels: R&B and Jazz.
to better exploit style correlations, we propose a novel deep learning approach with two parts: a label-graph based neural network, and a soft training mechanism with correlation based continuous label representation.