mlsanityData to 14 Oct 2026

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

arXiv 1911.02357 ↗First posted 6 Nov 2019cs.CVcited by 386

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Cited by 29 in Knowledge distillation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. Student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from natural images. This circumvents the need for prior data annotation. Anomalies are detected when the outputs of the student networks differ from that of the teacher network. This happens when they fail to generalize outside the manifold of anomaly-free training data. The intrinsic uncertainty in the student networks is used as an additional scoring function that indicates anomalies. We compare our method to a large number of existing deep learning based methods for unsupervised anomaly detection. Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.

Key facts. Cited by 386 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2019.

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Citing papers per year: 2021: 42; 2022: 64; 2023: 80; 2024: 95; 2025: 64; 2026: 41.

386 papers.

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  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  4. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  5. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
  6. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  7. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  8. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  9. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  10. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  11. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  12. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
38 more
  1. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  2. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  3. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  4. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 154
  5. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  6. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
  7. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  8. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  9. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  10. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  11. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  12. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  13. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  14. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  15. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  16. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  17. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  18. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 89
  19. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  20. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  21. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  22. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  23. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  24. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  25. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  26. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  27. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  28. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  29. 2023Few-Shot Defect Image Generation via Defect-Aware Feature Manipulationcited by 67
  30. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  31. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  32. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  33. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  34. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 63
  35. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 60
  36. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  37. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 57
  38. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56

50 most cited of 386 All 386 (JSON)