Meta-learning of semi-supervised learning from tasks with heterogeneous
  attribute spaces
Tomoharu Iwata Atsutoshi Kumagai
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.