{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:03:12Z","timestamp":1776830592185,"version":"3.51.2"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Chongqing Municipality","award":["CSTB2022NSCQ-MSX0786"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0786"]}]},{"name":"Humanities and Social Sciences Research Project of the Ministry of Education","award":["24YJA870003"],"award-info":[{"award-number":["24YJA870003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Industrial anomaly detection methods based on reverse distillation (RD) have shown significant potential. However, existing RD approaches struggle to achieve an effective balance between constraining the feature consistency of the teacher\u2013student networks and maintaining differentiated representation capability, which is crucial for precise anomaly detection. To address this challenge, we propose Reverse Distillation with Feature Reconstruction Enhancement (RD-RE) for Industrial Anomaly Detection. Firstly, we design a cross-stage feature fusion student network to integrate spatial detail information from the encoder with rich semantic information from the decoder. Secondly, we introduce a Locally Aware Dynamic Attention (LDA) module to enhance local detail feature response, thereby improving the model\u2019s robustness in capturing anomalous regions. Finally, a Context-Aware Adaptive Multi-Scale Feature Fusion (CFFMS-FF) module is designed to constrain the consistency of local feature reconstruction. Experiments on the MVTec AD benchmark dataset demonstrate the effectiveness of RD-RE, achieving competitive results of 99.0%, 95.8%, 78.3%, and 99.7% on pixel-level AUROC, PRO, and AP and image-level AUROC metrics, and outperforming existing RD-based approaches. These results conclude that the integration of cross-stage fusion and local attention effectively mitigates the representation-consistency trade-off, providing a more robust solution for industrial anomaly localization.<\/jats:p>","DOI":"10.3390\/computers15010021","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T10:03:48Z","timestamp":1767607428000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["RD-RE: Reverse Distillation with Feature Reconstruction Enhancement for Industrial Anomaly Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Youjia","family":"Fu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antao","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5557","DOI":"10.1109\/TNNLS.2021.3071026","article-title":"Multipixel anomaly detection with unknown patterns for hyperspectral imagery","volume":"33","author":"Liu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch, B., Brox, T., and Gehler, P. (2022, January 18\u201324). Towards total recall in industrial anomaly detection. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, C., Jiang, A., Feng, J., Zhang, Y., Wang, X., and Wang, Y. (2024, January 16\u201322). Adapting visual-language models for generalizable anomaly detection in medical images. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01081"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C. (2020, January 13\u201319). Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., and Rabiee, H.R. (2021, January 20\u201325). Multiresolution knowledge distillation for anomaly detection. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01466"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Deng, H., and Li, X. (2022, January 18\u201324). Anomaly detection via reverse distillation from one-class embedding. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tien, T.D., Nguyen, A.T., Tran, N.H., Huy, T.D., Duong, S.T., and Truong, S.Q.H. (2023, January 17\u201324). Revisiting reverse distillation for anomaly detection. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02348"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1109\/TCSVT.2023.3237562","article-title":"Visual anomaly detection via partition memory bank module and error estimation","volume":"33","author":"Xing","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Z., Xu, Y., and Hu, J. (2024, January 16\u201322). Hyperbolic anomaly detection. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01658"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tsai, M.-C., and Wang, S.-D. (2023, January 23\u201325). Self-supervised image anomaly detection and localization with synthetic anomalies. Proceedings of the 2023 10th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), San Antonio, TX, USA.","DOI":"10.1109\/IOTSMS59855.2023.10325818"},{"key":"ref_14","first-page":"4571","article-title":"A unified model for multi-class anomaly detection","volume":"Volume 35","author":"You","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, R., Zhang, J., Sun, J., and Zhang, C. (2023, January 17\u201324). Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02346"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., and Sko, D. (2021, January 10\u201317). Dr\u00c6m\u2013A discriminatively trained reconstruction embedding for surface anomaly detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A. (2014, January 23\u201328). Describing textures in the wild. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C. (2019, January 15\u201320). Mvtec ad\u2014Comprehensive real-world dataset for unsupervised anomaly detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zou, Y., Jeong, J., Pemula, L., Zhang, D., and Dabeer, O. (2022). Spot-the difference self-supervised pre-training for anomaly detection and segmentation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-20056-4_23"},{"key":"ref_21","first-page":"1","article-title":"Deep learning for unsupervised anomaly localization in industrial images: A survey","volume":"71","author":"Tao","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, S., Li, X., Huang, P., Shan, J., and Chen, T. (2023, January 17\u201324). Destseg: Segmentation guided denoising student-teacher for anomaly detection. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00381"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7943","DOI":"10.1609\/aaai.v39i8.32856","article-title":"Cnc: Cross-modal normality constraint for unsupervised multi-class anomaly detection","volume":"Volume 39","author":"Wang","year":"2025","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8472","DOI":"10.1609\/aaai.v38i8.28690","article-title":"A diffusion-based framework for multi-class anomaly detection","volume":"Volume 38","author":"He","year":"2024","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/1\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:25:39Z","timestamp":1767677139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/1\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,4]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["computers15010021"],"URL":"https:\/\/doi.org\/10.3390\/computers15010021","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,4]]}}}