mlsanityData to 14 Oct 2026

Task-Adaptive Pseudo Labeling for Transductive Meta-Learning

arXiv 2304.11173 ↗First posted 21 Apr 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Meta-learning performs adaptation through a limited amount of support set, which may cause a sample bias problem. To solve this problem, transductive meta-learning is getting more and more attention, going beyond the conventional inductive learning perspective. This paper proposes so-called task-adaptive pseudo labeling for transductive meta-learning. Specifically, pseudo labels for unlabeled query sets are generated from labeled support sets through label propagation. Pseudo labels enable to adopt the supervised setting as it is and also use the unlabeled query set in the adaptation process. As a result, the proposed method is able to deal with more examples in the adaptation process than inductive ones, which can result in better classification performance of the model. Note that the proposed method is the first approach of applying task adaptation to pseudo labeling. Experiments show that the proposed method outperforms the state-of-the-art (SOTA) technique in 5-way 1-shot few-shot classification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 21 Apr 2023.

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References · 20

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8 more
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