SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection | Paper Citation Record

Observation 74ddcf8f-4e3c-4388-b212-b1f168300dc3 · outbound

This paper cites Efficientad: Accurate visual anomaly detection at millisecond-level latencies.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Efficientad: Accurate visual anomaly detection at millisecond-level latencies

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Observation a7eac0e7-310e-47ae-8d47-fd8d422703ff · outbound

This paper cites Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

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Observation 0dbf1a15-ee99-45ea-aafc-e6f7ef271aab · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

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Observation 31a1ec43-ffda-401e-8239-65bd3dde297d · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings

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Observation c7d7a8f2-db7b-444f-b51f-bb87419db016 · outbound

This paper cites InProceedingsoftheIEEE/CVF internationalconferenceoncomputervision,pages9650–9660,2021.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection InProceedingsoftheIEEE/CVF internationalconferenceoncomputervision,pages9650–9660,2021

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Observation fec128ac-6cf5-43fa-aeee-9feb72dbd346 · outbound

This paper cites Detecting anomalous structures by convolutional sparse models.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Detecting anomalous structures by convolutional sparse models

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Observation f1234fad-e944-4563-a570-46ccccccca9b · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Padim: a patch distribution modeling framework for anomaly detection and localization

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Observation 9b8fc930-637b-48f4-998d-9388ee0759cb · outbound

This paper cites Anomaly detection via reverse distil- lation from one-class embedding.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Anomaly detection via reverse distil- lation from one-class embedding

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Observation 1d44cd73-a113-4543-ba75-3093edec7b10 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Observation 7832f2a2-d7ba-4f36-921f-cf571d41dede · outbound

This paper cites Few-shot defect image generation via defect-aware feature manipulation.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Few-shot defect image generation via defect-aware feature manipulation

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Observation 05c2c989-f0a5-4bc9-81b5-696d38b66de4 · outbound

This paper cites Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction

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Observation 8e1a904c-e745-4600-814e-bffe8ab9fdd6 · outbound

This paper cites Recon- trast: Domain-specific anomaly detection via contrastive reconstruc- tion.AdvancesinNeuralInformationProcessingSystems,36:10721– 10740, 2023.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Recon- trast: Domain-specific anomaly detection via contrastive reconstruc- tion.AdvancesinNeuralInformationProcessingSystems,36:10721– 10740, 2023

Reference 12

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Observation 3ba1d571-fc9b-44f9-ae3b-f7073feb7f8d · outbound

This paper cites Dinomaly: The less is more philosophy in multi-class unsuper- visedanomalydetection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Dinomaly: The less is more philosophy in multi-class unsuper- visedanomalydetection

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Observation f4d7bed5-b5e5-491b-8bde-41f416cae6f4 · outbound

This paper cites Con- trolling neural collapse enhances out-of-distribution detection and transfer learning.arXiv preprint arXiv:2502.10691, 2025.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Con- trolling neural collapse enhances out-of-distribution detection and transfer learning.arXiv preprint arXiv:2502.10691, 2025

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Observation 9cf0e6ff-efd8-4ca2-bfdd-a13b4def3a4d · outbound

This paper cites Mambaad: Exploring state space models for multi-class unsupervised anomaly detection.Advances in Neural Information Processing Systems, 37:71162–71187, 2024.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Mambaad: Exploring state space models for multi-class unsupervised anomaly detection.Advances in Neural Information Processing Systems, 37:71162–71187, 2024

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Observation 971dfe2d-28f7-442a-84bd-96cbfe573bf2 · outbound

This paper cites Adiffusion- based framework for multi-class anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Adiffusion- based framework for multi-class anomaly detection

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Observation e4e76e0f-28d8-457f-a50b-1dcd67e07bc4 · outbound

This paper cites Vlmdiff:Leveragingvision-languagemodelsformulti-classanomaly detection with diffusion, 2025.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Vlmdiff:Leveragingvision-languagemodelsformulti-classanomaly detection with diffusion, 2025

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Observation 6e676992-714e-4239-8025-82bca22d2104 · outbound

This paper cites Registrationbasedfew-shotanomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Registrationbasedfew-shotanomaly detection

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Observation a0ef5c18-f90a-4510-9896-b2108c7a6c6e · outbound

This paper cites Adversarial discriminative attention for robust anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Adversarial discriminative attention for robust anomaly detection

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Observation c768bb08-3ca9-48b4-b4f7-ec66010c905a · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and local- ization.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Cutpaste: Self-supervised learning for anomaly detection and local- ization

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Observation 3545bbfa-10fd-41d9-8d1c-0a68f7723e0e · outbound

This paper cites Ipg-frn: Intrinsic prototype-guided feature reconstruction network for industrial anomaly detection.Expert Systems with Ap- plications, page 132147, 2026.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Ipg-frn: Intrinsic prototype-guided feature reconstruction network for industrial anomaly detection.Expert Systems with Ap- plications, page 132147, 2026

Reference 21

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Observation d1ef2d3d-ca4e-47ed-a4c0-db5eb5bd8983 · outbound

This paper cites Swintransformerv2: Scaling up capacity and resolution.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Swintransformerv2: Scaling up capacity and resolution

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Observation fe13082c-7bd1-4427-8dfc-952e48150112 · outbound

This paper cites Sim- plenet: A simple network for image anomaly detection and localiza- tion.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Sim- plenet: A simple network for image anomaly detection and localiza- tion

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Observation 51052c37-29ca-4949-bd59-23cbfe84a08b · outbound

This paper cites Decoupled Weight Decay Regularization.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Decoupled Weight Decay Regularization

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Observation 2e77ea49-a60a-4227-a2b6-dcf5f0cd928f · outbound

This paper cites Patch distance based auto-encoder for industrial anomaly detection.Expert Systems with Applications, 270:126537, 2025.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Patch distance based auto-encoder for industrial anomaly detection.Expert Systems with Applications, 270:126537, 2025

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Observation eac09cd2-344e-49e4-beef-e527bc24f019 · outbound

This paper cites Ocgan: One- classnoveltydetectionusingganswithconstrainedlatentrepresenta- tions.InProceedingsoftheIEEE/CVFconferenceoncomputervision and pattern recognition, pages 2898–2906, 2019.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Ocgan: One- classnoveltydetectionusingganswithconstrainedlatentrepresenta- tions.InProceedingsoftheIEEE/CVFconferenceoncomputervision and pattern recognition, pages 2898–2906, 2019

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Observation 8100e311-b546-431c-b438-91999814d806 · outbound

This paper cites Towards total recall in industrial anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Towards total recall in industrial anomaly detection

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Observation adbaa80d-6b9d-49ce-b820-6cd6ba4e1033 · outbound

This paper cites Asymmetric student-teacher networks for industrial anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Asymmetric student-teacher networks for industrial anomaly detection

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Observation 767f418f-f444-440e-bd0b-059d16c78dc4 · outbound

This paper cites Imagenet large scale visual recognition challenge.Internationaljournalofcomputervision,115(3):211–252, 2015.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Imagenet large scale visual recognition challenge.Internationaljournalofcomputervision,115(3):211–252, 2015

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Observation 42504fba-8a94-4c3a-80a9-e668125ecaa9 · outbound

This paper cites Multiresolutionknowledge distillation for anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Multiresolutionknowledge distillation for anomaly detection

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Observation 0cacb31a-9495-483d-b7c8-cb4ee287cc36 · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection andlocalization.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Natural synthetic anomalies for self-supervised anomaly detection andlocalization

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Observation fb98a5be-14f4-4aaf-885f-dd6a82c59701 · outbound

This paper cites Real-iad:Areal-worldmulti-viewdatasetforbenchmarkingver- satile industrial anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Real-iad:Areal-worldmulti-viewdatasetforbenchmarkingver- satile industrial anomaly detection

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Observation 88100a41-abd9-47c8-bac6-bb66b21cb1e8 · outbound

This paper cites Student- teacher feature pyramid matching for anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Student- teacher feature pyramid matching for anomaly detection

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Observation bfbff450-ce7e-470e-8e1a-0d188f548944 · outbound

This paper cites Student-Teacher Feature Pyramid Matching for Anomaly Detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Student-Teacher Feature Pyramid Matching for Anomaly Detection

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Observation 099a4abd-32e1-4e7c-8cf5-3a1f1626164b · outbound

This paper cites Uninet: A contrastive learning-guidedunifiedframeworkwithfeatureselectionforanomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Uninet: A contrastive learning-guidedunifiedframeworkwithfeatureselectionforanomaly detection

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Observation 2bd6df22-10dc-42bc-94a0-787484993800 · outbound

This paper cites DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

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Observation 7a4171ba-8603-48c1-a703-68f28dcc4921 · outbound

This paper cites LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection

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Observation 09e69f07-ba31-4b61-81bd-8f3fdf9d8668 · outbound

This paper cites A unified model for multi-class anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection A unified model for multi-class anomaly detection

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Observation 0e0f07be-90d7-4fac-b9fc-bad54d618d6f · outbound

This paper cites Draem - a dis- criminatively trained reconstruction embedding for surface anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Draem - a dis- criminatively trained reconstruction embedding for surface anomaly detection

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Observation dfe4b877-ec05-4f19-9f73-55f796a502bd · outbound

This paper cites Dsr–a dual subspace re-projection network for surface anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Dsr–a dual subspace re-projection network for surface anomaly detection

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Observation 57fa82e9-d63b-43cb-a2fe-77efbf09c258 · outbound

This paper cites A diverse embedding-based composite reconstruction encoder– decoder for color fabric defect detection.Expert Systems with Applications, 278:127261, 2025.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection A diverse embedding-based composite reconstruction encoder– decoder for color fabric defect detection.Expert Systems with Applications, 278:127261, 2025

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Observation 7c89c6d2-3ad2-4a7b-bf7d-5b22723fadde · outbound

This paper cites A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection

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Observation 99545fb3-627e-4d42-b203-b51f57e25a9d · outbound

This paper cites Exploring plain vit features for multi-class unsupervised visual anomaly detec- tion.ComputerVisionandImageUnderstanding,253:104308,2025.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Exploring plain vit features for multi-class unsupervised visual anomaly detec- tion.ComputerVisionandImageUnderstanding,253:104308,2025

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Observation 10179101-dca3-4051-810c-c705fd712d14 · outbound

This paper cites Adaptive frequency modulated transformer for industrialsurfacedefectdetection.ExpertSystemswithApplications, page 132502, 2026.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Adaptive frequency modulated transformer for industrialsurfacedefectdetection.ExpertSystemswithApplications, page 132502, 2026

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Observation ba961156-4514-4636-9690-b6eb8208d326 · outbound

This paper cites Destseg: Segmentation guided denoising student-teacher for anomaly detection.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Destseg: Segmentation guided denoising student-teacher for anomaly detection

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Observation 9a85d9d3-965a-4573-b0ff-1ef8e1d22e9d · outbound

This paper cites Omnial: A unified cnn framework for unsupervised anomaly localization.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Omnial: A unified cnn framework for unsupervised anomaly localization

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Observation f1db2ed3-dbaf-42f7-9870-797af453f787 · outbound

This paper cites Fad:Featureaugmenteddistillationforanomaly detection and localization.Expert Systems with Applications, 288: 128249, 2025.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Fad:Featureaugmenteddistillationforanomaly detection and localization.Expert Systems with Applications, 288: 128249, 2025

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Observation 56b664ce-3faa-421e-b62e-db3b64ff8408 · outbound

This paper cites Class- incremental learning via dual augmentation.Advances in neural information processing systems, 34:14306–14318, 2021.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Class- incremental learning via dual augmentation.Advances in neural information processing systems, 34:14306–14318, 2021

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Observation 65bcf891-b8cc-4ccd-a8a6-e6dff9081337 · outbound

This paper cites Spot-the-difference self-supervised pre-training for anomaly detection and segmentation.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection Spot-the-difference self-supervised pre-training for anomaly detection and segmentation

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