  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.
