Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces
This addresses a limitation in meta-learning for semi-supervised learning, enabling broader task variety, but it is incremental as it builds on prior meta-learning frameworks.
The paper tackles the problem of semi-supervised meta-learning across tasks with heterogeneous attribute spaces, where existing methods assume shared spaces, and demonstrates that the proposed method outperforms existing approaches in classification and regression datasets.
We propose a meta-learning method for semi-supervised learning that learns from multiple tasks with heterogeneous attribute spaces. The existing semi-supervised meta-learning methods assume that all tasks share the same attribute space, which prevents us from learning with a wide variety of tasks. With the proposed method, the expected test performance on tasks with a small amount of labeled data is improved with unlabeled data as well as data in various tasks, where the attribute spaces are different among tasks. The proposed method embeds labeled and unlabeled data simultaneously in a task-specific space using a neural network, and the unlabeled data's labels are estimated by adapting classification or regression models in the embedding space. For the neural network, we develop variable-feature self-attention layers, which enable us to find embeddings of data with different attribute spaces with a single neural network by considering interactions among examples, attributes, and labels. Our experiments on classification and regression datasets with heterogeneous attribute spaces demonstrate that our proposed method outperforms the existing meta-learning and semi-supervised learning methods.