Papers › ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Eu Wern Teh, Terrance DeVries, Graham W. Taylor
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several enhancements. We find that low temperature scaling is a performance-critical component and explain why it works. Besides, we also discover that Global Max Pooling works better in general when compared to Global Average Pooling. Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling. Our enhanced model, called ProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1 across four different zero-shot retrieval datasets compared to the original ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2, 90.1, 81.4, and 90.9, respectively.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | CARS196 | ProxyNCA++ | R@1 | 90.1 | #2 of 8 | Archive leaderboard | report |
| Image Retrieval | CUB-200-2011 | ProxyNCA++ | R@1 | 72.2 | #3 of 8 | Archive leaderboard | report |
| Image Retrieval | In-Shop | ProxyNCA++ | R@1 | 90.9 | #3 of 7 | Archive leaderboard | report |
| Image Retrieval | SOP | ProxyNCA++ | R@1 | 81.4 | #5 of 14 | Archive leaderboard | report |
| Metric Learning | CARS196 | ResNet-50 + ProxyNCA++ | R@1 | 86.5 | #23 of 36 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | ResNet-50 + ProxyNCA++ | R@1 | 69.0 | #15 of 30 | Archive leaderboard | report |
| Metric Learning | In-Shop | ResNet-50 + ProxyNCA++ | R@1 | 90.9 | #12 of 15 | Archive leaderboard | report |
| Metric Learning | Stanford Online Products | ResNet-50 + ProxyNCA++ | R@1 | 80.7 | #21 of 33 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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