Papers › Asymmetric Loss For Multi-Label Classification

Asymmetric Loss For Multi-Label Classification

29 Sep 2020ICCV 2021 10arXiv:2009.14119archive 2025-07-28

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.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2009.14119")

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.

By repository: community (archive-listed): 12 samples from 2 repositories, 4 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

Alibaba-MIIL/ASL officialmentioned in papermentioned on GitHubpytorch report
Alibaba-MIIL/TResNet mentioned on GitHubpytorchApache-2.0 report
SlongLiu/ASL_reproduce mentioned on GitHubpytorchMIT report
kalelpark/ral mentioned on GitHubpytorch report
mrT23/TResNet mentioned on GitHubpytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

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.

2ran · our draft was wrong
1ran · fixture could not drive it
1ran
8unverified

Licence: 7 of the 12 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

average_precision SlongLiu/ASL_reproduce/src/helper_functions/helper_functions.py community (archive-listed) ran · fixture could not drive it fingerprinted MIT recorded; this copy not marked cleared · pointer only · f52b226226096fdd · report
conv2d SlongLiu/ASL_reproduce/src/models/tresnet/tresnet.py community (archive-listed) ran · our draft was wrong MIT recorded; this copy not marked cleared · pointer only · ad1cabd96edaffc7 · report
conv3x3 mrT23/TResNet/src/models/tresnet_v2/tresnet_v2.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 583f9780bdd00a45 · report
parse_args SlongLiu/ASL_reproduce/src/helper_functions/helper_functions.py community (archive-listed) ran MIT recorded; this copy not marked cleared · pointer only · 044fe3f67aac5e39 · report
accuracy mrT23/TResNet/src/helper_functions/helper_functions.py community (archive-listed) unverified Apache-2.0 (permissive) · 4d3faaaa1c706b0b · report
conv3x3_depth mrT23/TResNet/src/models/tresnet_v2/tresnet_v2.py community (archive-listed) unverified Apache-2.0 (permissive) · 92502509d71ac473 · report
create_dataloader mrT23/TResNet/src/helper_functions/helper_functions.py community (archive-listed) unverified Apache-2.0 (permissive) · df7d18ef933d037a · report
create_model SlongLiu/ASL_reproduce/src/models/utils/factory.py community (archive-listed) unverified MIT recorded; this copy not marked cleared · pointer only · f046cb82f2f2f60a · report
mAP SlongLiu/ASL_reproduce/src/helper_functions/helper_functions.py community (archive-listed) unverified MIT recorded; this copy not marked cleared · pointer only · 0759c5625039d127 · report
setup_logger SlongLiu/ASL_reproduce/src/helper_functions/logger.py community (archive-listed) unverified MIT recorded; this copy not marked cleared · pointer only · 55faa58ee2b0d1b7 · report
str_to_list SlongLiu/ASL_reproduce/src/helper_functions/nuswide_asl.py community (archive-listed) unverified MIT recorded; this copy not marked cleared · pointer only · 50a335a67cb29af0 · report
validate mrT23/TResNet/src/helper_functions/helper_functions.py community (archive-listed) unverified Apache-2.0 (permissive) · db31998e52bea4c8 · report

Tasks

ClassificationGeneral ClassificationImage ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label ClassificationObject Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections