Name
Abstract | ||
|---|---|---|
Annotated data samples in real-world applications are often limited. Meta-learning, which utilizes prior knowledge learned from related tasks and generalizes to new tasks of limited supervised experience, is an effective approach for few-shot learning. However, standard meta-learning with globally shared knowledge cannot handle the task heterogeneity problem well, i.e., tasks lie in different distributions. Recent advances have explored several ways to trigger task-dependent initial parameters or metrics, in order to customize task-specific information. These approaches learn task contextual information from data, but ignore external domain knowledge that can help in the learning process. In this paper, we propose a task-adaptive network (TAdaNet) that makes use of a domain-knowledge graph to enrich data representations and provide task-specific customization. Specifically, we learn a task embedding that characterizes task relationships and tailors task-specific parameters, resulting in a task-adaptive metric space for classification. Experimental results on a few-shot image classification problem show the effectiveness of the proposed method. We also apply it on a real-world disease classification problem, and show promising results for clinical decision support.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3394486.3403230 | KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Virtual Event
CA
USA
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7998-4 | 3 |
PageRank | References | Authors |
0.40 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiuling Suo | 1 | 36 | 7.02 |
| Jingyuan Chou | 2 | 10 | 2.57 |
| Weida Zhong | 3 | 5 | 0.77 |
| Aidong Zhang | 4 | 2970 | 405.63 |