Papers › Sampling Matters in Deep Embedding Learning
Sampling Matters in Deep Embedding Learning
Chao-yuan Wu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl
Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | CARS196 | Margin | R@1 | 86.9 | #5 of 8 | Archive leaderboard | report |
| Metric Learning | CARS196 | ResNet-50 + Margin | R@1 | 79.6 | #33 of 36 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | ResNet-50 + Margin | R@1 | 63.6 | #24 of 30 | 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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