It is a usual practice to ignore any structural information underlying classes in multi-class classification. In this paper, we propose a graph convolutional network (GCN) augmented neural network classifier to exploit a known, underlying graph structure of labels. The proposed approach resembles an (approximate) inference procedure in, for instance, a conditional random field (CRF), however without losing any modelling flexibility. The proposed method can easily scale up to thousands of labels. We evaluate the proposed approach on the problems of document classification and object recognition and report both accuracies and graphtheoretic metrics that correspond to the consistency of the model's prediction. The experiment results reveal that the proposed model outperforms a baseline method which ignores the graph structures of a label space.
Authors of REF apply Graph Convolutional Networks for learning of the relations between different tags in the form of a graph structure.
Graph Convolutional Networks for Classification with a Structured Label Space
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