</> Code with Papers by Syntology

Papers › Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

25 May 2018ICLR 2019 5arXiv:1805.10002archive 2025-07-28

Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang

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.

PaperPDFConference PDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

csyanbin/TPN-pytorch officialpytorch report
csyanbin/TPN tf report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Few-Shot Image ClassificationFew-Shot LearningGeneral ClassificationMeta-Learninggraph construction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Image Classification Mini-Imagenet 10-way (1-shot) TPN (Higher Shot) Accuracy 38.4 #5 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (1-shot) Label Propagation Accuracy 35.2 #7 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (5-shot) TPN (Higher Shot) Accuracy 52.8 #6 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (5-shot) Label Propagation Accuracy 51.2 #7 of 14 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (1-shot) TPN (Higher Shot) Accuracy 44.8 #5 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (1-shot) Label Propagation Accuracy 39.4 #6 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (5-shot) TPN (Higher Shot) Accuracy 59.4 #5 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (5-shot) Label Propagation Accuracy 57.9 #8 of 13 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Transductive Inference

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections