{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/same-same-but-differnet-semi-supervised","title":"Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows","arxiv_id":"2008.12577","date":"2020-08-28","proceeding":null,"authors":["Marco Rudolph","Bastian Wandt","Bodo Rosenhahn"],"abstract":"The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.","url_abs":"https://arxiv.org/abs/2008.12577v1","url_pdf":"https://arxiv.org/pdf/2008.12577v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"same-same-but-differnet-semi-supervised","repo_url":"https://github.com/marco-rudolph/differnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"same-same-but-differnet-semi-supervised","repo_url":"https://github.com/MattSkiff/cow_flow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"same-same-but-differnet-semi-supervised","repo_url":"https://github.com/kuangzijian/Computer-Vision-and-Normalizing-Flow-Based-Defect-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"defect-detection","task_name":"Defect Detection"},{"task_slug":"supervised-defect-detection","task_name":"Supervised Defect Detection"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"affine-coupling","method_name":"Affine Coupling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"differnet","method_name":"DifferNet"},{"method_slug":"normalizing-flows","method_name":"Normalizing Flows"},{"method_slug":"realnvp","method_name":"RealNVP"}],"datasets_introduced":[],"methods_introduced":[{"slug":"differnet","name":"DifferNet","full_name":"DifferNet"}],"results":[{"leaderboard":"/sota/anomaly-detection-on-insplad","task":"Anomaly Detection","dataset":"InsPLAD","model":"DifferNet","rank_in_archive_order":2,"of":5,"metrics":{"Detection AUROC":"92.46"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"DifferNet","rank_in_archive_order":95,"of":148,"metrics":{"Detection AUROC":"94.9","FPS":"2"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-surface-defect-saliency","task":"Anomaly Detection","dataset":"Surface Defect Saliency of Magnetic Tile","model":"DifferNet (unsupervised)","rank_in_archive_order":3,"of":4,"metrics":{"Detection AUROC":"97.7"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2008.12577","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}