Learning to propagate labels: Transductive propagation network for few-shot learning
- Publication Type:
- Conference Proceeding
- Citation:
- 7th International Conference on Learning Representations, ICLR 2019, 2019
- Issue Date:
- 2019-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| learning_to_propagate_labels_transductive_propagation_network_for_few_shot_learning.pdf | Published version | 1.75 MB |
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© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task. Yet, even with such meta-learning, the low-data problem in the novel classification task still remains. In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data. TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner. We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.
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