mlsanityData to 14 Oct 2026

DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

arXiv 2211.11317 ↗First posted 21 Nov 2022cs.CVcited by 154

Not on the map. Why?

Abstract (arXiv)

Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we introduce a denoising procedure that allows the student network to learn more robust representations. From synthetically corrupted normal images, we train the student network to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision.

Key facts. Cited by 154 papers in the map (data through 14 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 21 Nov 2022.

Builds on

33 of 43 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  4. 2013Describing Textures in the Wildcited by 2,125
  5. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  6. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  7. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  8. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  9. 2017Focal Loss for Dense Object Detectioncited by 752
  10. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  11. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
21 more
  1. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  2. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  3. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  4. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  5. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  6. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  7. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  8. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  9. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  10. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  11. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  12. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
  13. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  14. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  15. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  16. 2020Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detectioncited by 83
  17. 2018q-Space Novelty Detection with Variational Autoencoderscited by 22
  18. 2021AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learningcited by 21
  19. 2021Reconstruction Student with Attention for Student-Teacher Pyramid Matchingcited by 20
  20. 2018Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?cited by 17
  21. 2019Attribute Restoration Framework for Anomaly Detectioncited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 13; 2024: 45; 2025: 57; 2026: 39.

154 papers.

Sort:

By area:

  1. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  2. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  3. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  4. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  5. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
  6. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  7. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 73
  8. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  9. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 48
  10. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  11. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  12. 2023Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detectioncited by 39
38 more
  1. 2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detectioncited by 36
  2. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  3. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
  4. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 26
  5. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  6. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  7. 2024Open-World Semantic Segmentation Including Class Similaritycited by 24
  8. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  9. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  10. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  11. 2025Learning to Detect Multi-class Anomalies with Just One Normal Image Promptcited by 18
  12. 2024Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detectioncited by 17
  13. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 16
  14. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  15. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  16. 2024SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detectioncited by 12
  17. 2025Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detectioncited by 11
  18. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 10
  19. 2024DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learningcited by 10
  20. 2024Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detectioncited by 10
  21. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  22. 2024FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Datacited by 8
  23. 2024Unlocking the Potential of Reverse Distillation for Anomaly Detectioncited by 8
  24. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  25. 2023No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detectioncited by 7
  26. 2024A SAM-guided Two-stream Lightweight Model for Anomaly Detectioncited by 7
  27. 2024A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detectioncited by 7
  28. 2024ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detectioncited by 7
  29. 2024Promptable Anomaly Segmentation with SAM Through Self-Perception Tuningcited by 7
  30. 2025Towards Accurate Unified Anomaly Segmentationcited by 7
  31. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  32. 2025INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learningcited by 7
  33. 2025AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptationcited by 7
  34. 2023Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformerscited by 6
  35. 2024A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptationcited by 6
  36. 2024PQD: Post-training Quantization for Efficient Diffusion Modelscited by 6
  37. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  38. 2025Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memorycited by 6

50 most cited of 154 All 154 (JSON)