{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/edge-labeling-graph-neural-network-for-few","title":"Edge-labeling Graph Neural Network for Few-shot Learning","arxiv_id":"1905.01436","date":"2019-05-04","proceeding":"CVPR 2019 6","authors":["Jongmin Kim","Taesup Kim","Sungwoong Kim","Chang D. Yoo"],"abstract":"In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-cluster similarity and the inter-cluster dissimilarity. In contrast, the proposed EGNN learns to predict the edge-labels rather than the node-labels on the graph that enables the evolution of an explicit clustering by iteratively updating the edge-labels with direct exploitation of both intra-cluster similarity and the inter-cluster dissimilarity. It is also well suited for performing on various numbers of classes without retraining, and can be easily extended to perform a transductive inference. The parameters of the EGNN are learned by episodic training with an edge-labeling loss to obtain a well-generalizable model for unseen low-data problem. On both of the supervised and semi-supervised few-shot image classification tasks with two benchmark datasets, the proposed EGNN significantly improves the performances over the existing GNNs.","url_abs":"https://arxiv.org/abs/1905.01436v1","url_pdf":"https://arxiv.org/pdf/1905.01436v1.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":"edge-labeling-graph-neural-network-for-few","repo_url":"https://github.com/khy0809/fewshot-egnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"edge-labeling-graph-neural-network-for-few","repo_url":"https://github.com/xxxnhb/fewshot-egnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"edge-labeling-graph-neural-network-for-few","repo_url":"https://github.com/yjt2018/fewshot-egnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"edge-labeling-graph-neural-network-for-few","repo_url":"https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/EGNN%20Mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","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-neural-network","task_name":"Graph Neural Network"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-mini-3","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (5-shot)","model":"EGNN + Transduction","rank_in_archive_order":66,"of":95,"metrics":{"Accuracy":"76.37"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-tiered-imagenet-5-way","task":"Image Classification","dataset":"Tiered ImageNet 5-way (5-shot)","model":"EGNN+Transduction","rank_in_archive_order":1,"of":7,"metrics":{"Accuracy":"80.15"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1905.01436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.01436"}},"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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