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. 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.
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. 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.
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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
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
R
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
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