Papers › CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

8 Apr 2021CVPR 2021 1arXiv:2104.04015archive 2025-07-28

Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister

We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-theart 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training.

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="2104.04015")

Code

Syntology Ran 3 of 3 code samples harvested from 2 repositories linked to this paper; 0 have no recorded run. Of those that ran: 3 ran with no contract checked.

By repository: community (archive-listed): 3 samples from 2 repositories, 3 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

LilitYolyan/CutPaste mentioned on GitHubpytorch report
Runinho/pytorch-cutpaste mentioned on GitHubpytorch 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

3 samples harvested; 3 ran; 0 honoured the contract we drafted; 0 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.

3ran

Licence: 2 of the 3 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.

CutPaste LilitYolyan/CutPaste/cutpaste.py community (archive-listed) ran MIT (permissive) · 59401358a018441f · report
CutPaste Runinho/pytorch-cutpaste/cutpaste.py community (archive-listed) ran no licence file found · pointer only · cb970ddafdfccb8c · report
CutPasteNormal Runinho/pytorch-cutpaste/cutpaste.py community (archive-listed) ran no licence file found · pointer only · 32ee54e0db9f9970 · report

Tasks

Anomaly ClassificationAnomaly DetectionData AugmentationDefect DetectionOne-class classifierSelf-Supervised LearningTransfer LearningUnsupervised Anomaly Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Classification GoodsAD CutPaste AUPR 62.8 #10 of 11 Archive leaderboard report
Anomaly Classification GoodsAD CutPaste AUROC 60.2 #10 of 11 Archive leaderboard report
Anomaly Detection MVTec AD CutPaste (ensemble) Detection AUROC 96.1 #84 of 148 Archive leaderboard report
Anomaly Detection MVTec AD CutPaste (Image level detector) Detection AUROC 95.2 #92 of 148 Archive leaderboard report
Anomaly Detection MVTec AD CutPaste (Image level detector) Segmentation AUROC 88.3 #92 of 148 Archive leaderboard report
Anomaly Detection MVTec AD CutPaste (Patch level detector) Segmentation AUROC 96.0 #137 of 148 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