{"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/deep-comparison-relation-columns-for-few-shot","title":"RelationNet2: Deep Comparison Columns for Few-Shot Learning","arxiv_id":"1811.07100","date":"2018-11-17","proceeding":null,"authors":["Xueting Zhang","Yu-ting Qiang","Flood Sung","Yongxin Yang","Timothy M. Hospedales"],"abstract":"Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is based on metric learning, which trains a deep embedding to support image similarity matching. Our insight is that effective general purpose matching requires non-linear comparison of features at multiple abstraction levels. We thus propose a new deep comparison network comprised of embedding and relation modules that learn multiple non-linear distance metrics based on different levels of features simultaneously. Furthermore, to reduce over-fitting and enable the use of deeper embeddings, we represent images as distributions rather than vectors via learning parameterized Gaussian noise regularization. The resulting network achieves excellent performance on both miniImageNet and tieredImageNet.","url_abs":"https://arxiv.org/abs/1811.07100v3","url_pdf":"https://arxiv.org/pdf/1811.07100v3.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":"deep-comparison-relation-columns-for-few-shot","repo_url":"https://github.com/zhangxueting/DCN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-comparison-relation-columns-for-few-shot","repo_url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/relationnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-mini-7","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 20-way (1-shot)","model":"Deep Comparison Network","rank_in_archive_order":2,"of":6,"metrics":{"Accuracy":"32.07"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-8","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 20-way (5-shot)","model":"Deep Comparison Network","rank_in_archive_order":2,"of":6,"metrics":{"Accuracy":"47.31"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-2","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (1-shot)","model":"Deep Comparison Network","rank_in_archive_order":65,"of":105,"metrics":{"Accuracy":"62.88"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-3","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (5-shot)","model":"Deep Comparison Network","rank_in_archive_order":68,"of":95,"metrics":{"Accuracy":"75.84"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-tiered","task":"Few-Shot Image Classification","dataset":"Tiered ImageNet 5-way (1-shot)","model":"Deep Comparison Network","rank_in_archive_order":37,"of":49,"metrics":{"Accuracy":"68.83"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-tiered-1","task":"Few-Shot Image Classification","dataset":"Tiered ImageNet 5-way (5-shot)","model":"Deep Comparison Network","rank_in_archive_order":46,"of":51,"metrics":{"Accuracy":"79.62"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.07100","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}