Multi-task Learning of Classification and Generation for Set-structured Data

In this study, we propose a multi-task learning model of classification and generation for set-structured data. The proposed model learns data generation and classification in a single neural network by integrating a classification layer into a variational autoencoder while maintaining permutation invariance and equivariance nature, which are charac-teristics of set-structured data. The proposed model allows for semi-supervised learning in set-structured data classifi-cation and can also be applied to confidence calibration using the input data distribution estimated by the generative model. In the experiments, we evaluated the performance of the proposed model in a semi-supervised classification task on set-structured datasets and compared it with a baseline model consisting only of a classifier. The results demon-strated that simultaneous learning of the classification and generation effectively improves the classification accuracy and confidence reliability for set-structured data, even with a limited number of labeled data.