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Classic methods on semi-supervised learning that have\nfocused on transductive learning have not been fully exploited in the inductive\nframework followed by modern deep learning. The same holds for the manifold\nassumption---that similar examples should get the same prediction. In this\nwork, we employ a transductive label propagation method that is based on the\nmanifold assumption to make predictions on the entire dataset and use these\npredictions to generate pseudo-labels for the unlabeled data and train a deep\nneural network. At the core of the transductive method lies a nearest neighbor\ngraph of the dataset that we create based on the embeddings of the same\nnetwork.Therefore our learning process iterates between these two steps. We\nimprove performance on several datasets especially in the few labels regime and\nshow that our work is complementary to current state of the art.","url_abs":"http://arxiv.org/abs/1904.04717v1","url_pdf":"http://arxiv.org/pdf/1904.04717v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"label-propagation-for-deep-semi-supervised","repo_url":"https://github.com/ahmetius/LP-DeepSSL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"transductive-learning","task_name":"Transductive Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.04717","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04717"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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