Papers › Asymmetric Loss For Multi-Label Classification
Asymmetric Loss For Multi-Label Classification
Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, Lihi Zelnik-Manor
In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, and can lead to under-emphasizing gradients from positive labels during training, resulting in poor accuracy. In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples. The loss enables to dynamically down-weights and hard-thresholds easy negative samples, while also discarding possibly mislabeled samples. We demonstrate how ASL can balance the probabilities of different samples, and how this balancing is translated to better mAP scores. With ASL, we reach state-of-the-art results on multiple popular multi-label datasets: MS-COCO, Pascal-VOC, NUS-WIDE and Open Images. We also demonstrate ASL applicability for other tasks, such as single-label classification and object detection. ASL is effective, easy to implement, and does not increase the training time or complexity. Implementation is available at: https://github.com/Alibaba-MIIL/ASL.
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Code
Syntology Ran 4 of 12 code samples harvested from 2 repositories linked to this paper; 8 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 1 ran · fixture could not drive it; 1 ran with no contract checked.
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Code Syntology ran Syntology
12 samples harvested; 4 ran; 0 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Label Classification | MS-COCO | TResNet-XL (resolution 640) | mAP | 88.4 | #15 of 34 | Archive leaderboard | report |
| Multi-Label Classification | MS-COCO | TResNet-L (resolution 448) | mAP | 86.6 | #20 of 34 | Archive leaderboard | report |
| Multi-Label Classification | NUS-WIDE | TResNet-L (resolution 448) | MAP | 65.2 | #4 of 9 | Archive leaderboard | report |
| Multi-Label Classification | OpenImages-v6 | TResNet-L | mAP | 86.3 | #4 of 4 | Archive leaderboard | report |
| Multi-Label Classification | PASCAL VOC 2007 | TResNet-L (resolution 448, pretrain from MS-COCO) | mAP | 95.8 | #8 of 17 | Archive leaderboard | report |
| Multi-Label Classification | PASCAL VOC 2007 | TResNet-L (resolution 448, pretrain from ImageNet) | mAP | 94.6 | #12 of 17 | 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.
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