Weakly Supervised Defect Detection

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (5 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.

• FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
  15 Nov 2021 5 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
• Mixed supervision for surface-defect detection: from weakly to fully supervised learning
  13 Apr 2021 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a…
• DSR -- A dual subspace re-projection network for surface anomaly detection
  2 Aug 2022 1 repository listed
  The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images.
• S2D2Net: An Improved Approach For Robust Steel Surface Defects Diagnosis With Small Sample Learning
  23 Aug 2021 1 repository listed
  Surface defect recognition of products is a necessary process to guarantee the quality of industrial production.

Syntology lines on 2 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 2026-09-25.