Label Error Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Identifying Incorrect Annotations in Multi-Label Classification Data
  25 Nov 2022 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes).
• WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
  7 Oct 2021 2 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hours unlabeled speech, with 22400+ hours…
• CleanPatrick: A Benchmark for Image Data Cleaning
  16 May 2025 1 repository listed
  Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance.
• AQuA: A Benchmarking Tool for Label Quality Assessment
  15 Jun 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning…
• Identifying Label Errors in Object Detection Datasets by Loss Inspection
  13 Mar 2023 1 repository listed Syntology ran 2 of 4 samples · 2 unverified
  In this work, we for the first time introduce a benchmark for label error detection methods on object detection datasets as well as a label error detection method and a number of baselines.
• The Re-Label Method For Data-Centric Machine Learning
  9 Feb 2023 1 repository listed
  In industry deep learning application, our manually labeled data has a certain number of noisy data.
• CTRL: Clustering Training Losses for Label Error Detection
  17 Aug 2022 1 repository listed
  We propose a novel framework, called CTRL (Clustering TRaining Losses for label error detection), to detect label errors in multi-class datasets.
• Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification
  13 Jul 2022 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  In this work, we for the first time present a method for detecting label errors in image datasets with semantic segmentation, i.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.