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Metric Learning archive 2025-07-28

CARS196 Benchmark (Metric Learning)

36 rows 32 with code listed 1 metric Dataset page

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.

Source: Road Network Metric Learning for Estimated Time of Arrival

The archive carries no text for this table; the description above is the archive's text for the task Metric Learning. archive 2025-07-28

Over time archive 2025-07-28

The chart needs JavaScript; the table below carries every value.

Not inferred: R@1. Points are placed at the row's paper date; 36 of 36 rows carry one.

Results archive 2025-07-28

Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.

Paper Code Ran Syntology Report
1 Unicom+ViT-L@336px 98.2 ✓ Paper Code 2023 3 of 6 ran · 3 unverified report
2 Hyp-DINO 8x8 92.8 – Paper Code 2022 4 of 6 ran · 2 unverified report
3 NED 91.5 – Paper – 2020 no code linked report
4 ResNet-50 + Intra-Batch (ensemble of 5) 91.5 – Paper Code 2021 linked, not harvested report
5 ResNet-50 + AVSL 91.5 ✓ Paper Code 2022 3 of 4 ran · 1 unverified report
6 EfficientDML-VPTSP-G/512 91.2 ✓ Paper Code 2024 3 of 5 ran · 2 unverified report
7 CCL (ResNet-50) 91.02 – Paper – 2023 no code linked report
8 ResNet50 + Language 90.2 ✓ Paper Code 2022 4 of 5 ran · 1 unverified report
9 ResNet-50 + Metrix 89.6 ✓ Paper Code 2021 2 of 2 ran · 0 unverified report
10 ResNet50 + S2SD 89.5 ✓ Paper Code 2020 0 of 3 ran · 3 unverified report
11 Recall@k Surrogate loss (ViT-B/16) 89.5 – Paper Code 2021 0 of 11 ran · 11 unverified report
12 ResNet-50 + Cross-Entropy 89.3 ✓ Paper Code 2020 1 of 1 ran · 0 unverified report
13 Hyp-DINO 89.2 – Paper Code 2022 4 of 6 ran · 2 unverified report
14 ResNet50 + NIR 89.1 ✓ Paper Code 2022 2 of 3 ran · 1 unverified report
15 Margin + DAS 88.34 – Paper Code 2022 0 of 1 ran · 1 unverified report
16 BN-Inception + Proxy-Anchor 88.3 ✓ Paper Code 2020 linked, not harvested report
17 Recall@k Surrogate loss (ResNet-50) 88.3 – Paper Code 2021 0 of 11 ran · 11 unverified report
18 ResNet-50 + Intra-Batch 88.1 – Paper Code 2021 linked, not harvested report
19 ABE + HORDE 88.0 ✓ Paper Code 2019 0 of 8 ran · 8 unverified report
20 MS + SEC + DAS 87.8 – Paper Code 2022 0 of 1 ran · 1 unverified report
21 ResNet50 + DiVA 87.6 ✓ Paper Code 2020 3 of 5 ran · 2 unverified report
22 ProxyAnchor + DIML 87.01 ✓ Paper Code 2021 2 of 2 ran · 0 unverified report
23 ResNet-50 + ProxyNCA++ 86.5 ✓ Paper Code 2020 4 of 5 ran · 1 unverified report
24 Gradient Surgery 86.5 – Paper – 2022 no code linked report
25 Hyp-ViT 86.5 ✓ Paper Code 2022 4 of 6 ran · 2 unverified report
26 Group Loss 85.6 – Paper Code 2019 12 of 18 ran · 6 unverified report
27 ABE-8-512 85.2 ✓ Paper – 2018 no code linked report
28 BN-Inception + SoftTriple 84.5 ✓ Paper Code 2019 2 of 4 ran · 2 unverified report
29 ResNet50 (128) + PADS 83.5 ✓ Paper Code 2020 4 of 10 ran · 6 unverified report
30 CircleLoss 83.4 – Paper Code 2020 2 of 2 ran · 0 unverified report
31 EPSHN(512) 82.7 – Paper Code 2019 1 of 1 ran · 0 unverified report
32 ResNet50 (128) + MIC 82.6 ✓ Paper Code 2019 linked, not harvested report
33 ResNet-50 + Margin 79.6 ✓ Paper Code 2017 2 of 2 ran · 0 unverified report
34 GoogLeNet + HDML 79.1 ✓ Paper Code 2019 2 of 2 ran · 0 unverified report
35 EPSHN(64) 75.5 – Paper Code 2019 1 of 1 ran · 0 unverified report
36 SCT(64) 73.2 – Paper Code 2020 linked, not harvested report

All 36 rows shown. 36 link to a paper page on this site; 20 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28

Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 27 rows have a graph line, from 22 distinct papers; 21 rows (18 papers) have at least one sample that ran. Counting each paper once: Syntology ran 56 of 106 samples; 50 unverified. Separately, 15 of those 106 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-25. Per-sample status is on the paper page.

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