Before revision: Most of existing gradient-based meta-learning approaches to few-shot learning assume that all tasks have the same input feature space. However, in the real world scenarios, there are many cases that the input structures of tasks can be different, that is , different tasks may vary in the number of input modalities or the data structure of each modality. Existing gradient-based approaches cannot handle such heterogeneous task distribution (HTD) as different types of tasks only share partial meta-parameters.
After revision: In this paper, we study the problem of how to learn a model-agnostic meta-learner that simultaneously learning from different feature spaces. The reason that most of model-agnostic meta-learner methods cannot handle multiple task spaces is due to less common knowledge for the task instances. The reduction of shared knowledge is because different tasks with different example-level manifolds cannot entirely share the same model architecture. Actually, various tasks only share partial meta-parameters.