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.

翻译：我们提出一种面向半监督学习的元学习方法，该方法能从具有异质属性空间的多个任务中学习。现有的半监督元学习方法假设所有任务共享相同的属性空间，这阻碍了我们利用多样化的任务进行学习。采用所提出的方法，当任务间属性空间不同时，通过利用未标记数据及不同任务中的数据，能够提升标记数据量较少任务的预期测试性能。该方法通过神经网络将标记与未标记数据同步嵌入任务特定空间，并在该嵌入空间中通过适配分类或回归模型来估计未标记数据的标签。针对该神经网络，我们开发了可变特征自注意力层，它通过考虑样本、属性及标签之间的交互作用，使得单个神经网络能够处理具有不同属性空间的数据嵌入问题。我们在异质属性空间的分类与回归数据集上开展的实验表明，所提方法优于现有的元学习与半监督学习方法。