Before revision:  In this paper, we propose HetMAML, a task-heterogeneous meta-agnostic meta-learning framework that can generalize not only common meta-parameters shared across different types of tasks but also type-specific meta-parameters. Specifically, we design a multi-channel backbone module that encodes the input of each type of tasks into the samelength sequence of modality-specific embeddings. Then, we propose a task-aware multimodal encoder which can automatically take into account the context of task-specific input structures and adaptively project the heterogeneous input spaces to the same lower-dimensional concept space. The extensive experiments on five task-heterogeneous datasets demonstrate that our HetMAML successfully captures both type-specific and shared meta-parameters across heterogeneous tasks which fast adapt to all types of new tasks.
After revision:  For example, for two multi-feature tasks whose example-level manifolds contain a same subspace but their remaining subspaces are not the same, one can imagine that the common knowledge can be the feature extractor for that common subspace, but other subspaces' feature extractors cannot be used between the two tasks.