Label Propagation for Deep Semi-Supervised Learning
Open accessAbstract
Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on transductive learning have not been fully exploited in the inductive framework followed by modern deep learning. The same holds for the manifold assumption-that similar examples should get the same prediction. In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network. At the core of the transductive method lies a nearest neighbor graph of the dataset that we create based on the embeddings of the same network. Therefore our learning process iterates between these two steps. We improve performance on several datasets especially in the few labels regime and show that our work is complementary to current state of the art.
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- Authors
- Iscen, A., Tolias, G., Avrithis, Y., & Chum, O.
- Affiliations
- Czech Technical University in Prague (CZ)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Vision Recognition Group, Czech Technical Univ. in Prague
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5065-5074
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2936604471
- Cited by
- 686
- References
- 39
- Citations by year
- 2026: 32 · 2025: 81 · 2024: 124 · 2023: 108 · 2022: 111 · 2021: 124 · 2020: 93 · 2019: 13
- FWCI
- 32.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- semi-supervised learning, label propagation, transductive learning, inductive learning, manifold assumption, pseudo-labeling, unlabeled data, nearest neighbor graph
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