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We propose a meta-learning method for semi-supervised learning that learnsfrom multiple tasks with heterogeneous attribute spaces. The existingsemi-supervised meta-learning methods assume that all tasks share the sameattribute space, which prevents us from learning with a wide variety of tasks.With the proposed method, the expected test performance on tasks with a smallamount of labeled data is improved with unlabeled data as well as data invarious tasks, where the attribute spaces are different among tasks. Theproposed method embeds labeled and unlabeled data simultaneously in atask-specific space using a neural network, and the unlabeled data's labels areestimated by adapting classification or regression models in the embeddingspace. For the neural network, we develop variable-feature self-attentionlayers, which enable us to find embeddings of data with different attributespaces with a single neural network by considering interactions among examples,attributes, and labels. Our experiments on classification and regressiondatasets with heterogeneous attribute spaces demonstrate that our proposedmethod outperforms the existing meta-learning and semi-supervised learningmethods.

Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces