Collaborative learning of supervision and correlation for generalized zero-shot extreme multi-label learning

Generalized zero-shot extreme multi-label learning (GZXML) aims to predict relevant labels for unknown instances from a set of seen and unseen labels and is widely used in engineering applications. Since the supervisory information of the instances is incomplete in this task, the existing methods classify such instances based on the semantic relationships between the instances and labels. However, the supervisory information of the seen labels is also crucial for achieving high prediction performance. To bridge this gap, we propose collaborative learning of supervision and correlations for GZXML (CLSC-XML). CLSC-XML leverages both the semantic relationships between instances and labels and the supervisory information of the seen labels to enhance the prediction results for unseen labels. Specifically, CLSC-XML extracts discriminative and representational features, which are then fed into classification and correlation modules for collaborative learning. Furthermore, to enrich the incomplete supervised information, we propose the generation of features for unseen labels (GFUL) algorithm. The classifier is trained alternately with the GFUL algorithm. The classifier provides semantic guidance to the GFUL algorithm, and in turn, the GFUL algorithm helps the classification model enrich the supervised information. Experimental results show that CLSC-XML outperforms the state-of-the-art methods and requires less training time.