{"url":"/sota/metric-learning-on-dyml-product","task":{"name":"Metric Learning","url":"/task/metric-learning","note":null},"dataset":{"name":"DyML-Product","url":"/dataset/dyml-product"},"category":"Computer Vision","categories":["Computer Vision","Methodology"],"category_note":null,"description":"The goal of **Metric Learning** is to learn a representation function that maps objects into an embedded space. The distance in the embedded space should preserve the objects’ similarity — similar objects get close and dissimilar objects get far away. Various loss functions have been developed for Metric Learning. For example, the **contrastive loss** guides the objects from the same class to be mapped to the same point and those from different classes to be mapped to different points whose distances are larger than a margin. **Triplet loss** is also popular, which requires the distance between the anchor sample and the positive sample to be smaller than the distance between the anchor sample and the negative sample.\r\n\r\n\r\n<span class=\"description-source\">Source: [Road Network Metric Learning for Estimated Time of Arrival ](https://arxiv.org/abs/2006.13477)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Average-mAP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Average-mAP":"higher"}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":2},"rows":[{"rank_in_archive_order":1,"model":"HAPPIER","metrics":{"Average-mAP":"38.0"},"uses_additional_data":true,"paper_date":"2022-07-05","paper":"/paper/hierarchical-average-precision-training-for","paper_url":"https://arxiv.org/abs/2207.04873v2","paper_title":"Hierarchical Average Precision Training for Pertinent Image Retrieval","code":"https://github.com/elias-ramzi/happier","n_code_links":2,"syntology":{"n_ran":6,"n_unverified":3,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"CSL","metrics":{"Average-mAP":"28.7"},"uses_additional_data":true,"paper_date":"2021-03-22","paper":"/paper/dynamic-metric-learning-towards-a-scalable","paper_url":"https://arxiv.org/abs/2103.11781v1","paper_title":"Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales","code":"https://github.com/SupetZYK/DynamicMetricLearning","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. 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