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Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment function containing two terms: (1) a unary term assigning query samples to the nearest class prototype, and (2) a pairwise Laplacian term encouraging nearby query samples to have consistent label assignments. Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set. We derive a computationally efficient bound optimizer of a relaxation of our function, which computes independent (parallel) updates for each query sample, while guaranteeing convergence. Following a simple cross-entropy training on the base classes, and without complex meta-learning strategies, we conducted comprehensive experiments over five few-shot learning benchmarks. 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Furthermore, our transductive inference is very fast, with computational times that are close to inductive inference, and can be used for large-scale few-shot tasks.","url_abs":"https://arxiv.org/abs/2006.15486v3","url_pdf":"https://arxiv.org/pdf/2006.15486v3.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":"laplacian-regularized-few-shot-learning-1","repo_url":"https://github.com/imtiazziko/LaplacianShot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"laplacian-regularized-few-shot-learning-1","repo_url":"https://github.com/sicara/easy-few-shot-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[{"method_slug":"transductive-inference","method_name":"Transductive Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-4","task":"Few-Shot Image Classification","dataset":"Dirichlet CUB-200 (5-way, 1-shot)","model":"Laplacian-Shot","rank_in_archive_order":4,"of":8,"metrics":{"1:1 Accuracy":"73.7"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-5","task":"Few-Shot Image Classification","dataset":"Dirichlet CUB-200 (5-way, 5-shot)","model":"Laplacian-Shot","rank_in_archive_order":3,"of":8,"metrics":{"1:1 Accuracy":"87.7"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet","task":"Few-Shot Image Classification","dataset":"Dirichlet Mini-Imagenet (5-way, 1-shot)","model":"Laplacian-Shot","rank_in_archive_order":4,"of":12,"metrics":{"1:1 Accuracy":"65.4"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-1","task":"Few-Shot Image Classification","dataset":"Dirichlet Mini-Imagenet (5-way, 5-shot)","model":"Laplacian-Shot","rank_in_archive_order":3,"of":12,"metrics":{"1:1 Accuracy":"81.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-2","task":"Few-Shot Image Classification","dataset":"Dirichlet Tiered-Imagenet (5-way, 1-shot)","model":"Laplacian-Shot","rank_in_archive_order":5,"of":9,"metrics":{"1:1 Accuracy":"72.3"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-3","task":"Few-Shot Image Classification","dataset":"Dirichlet Tiered-Imagenet (5-way, 5-shot)","model":"Laplacian-Shot","rank_in_archive_order":3,"of":9,"metrics":{"1:1 Accuracy":"85.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.15486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15486"}},"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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