mlsanityData to 14 Oct 2026

SimpleNet: A Simple Network for Image Anomaly Detection and Localization

arXiv 2303.15140 ↗First posted 27 Mar 2023cs.CVcited by 256

Not on the map. Why?

Cited by 23 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is based on three intuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, generating synthetic anomalies in feature space is more effective, as defects may not have much commonality in the image space. Third, a simple discriminator is much efficient and practical. In spite of simplicity, SimpleNet outperforms previous methods quantitatively and qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomaly detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best performing model. Furthermore, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in performance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet.

Key facts. Cited by 256 papers in the map (data through 14 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

23 of 33 references.

References · 23

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2015Variational Inference with Normalizing Flowscited by 2,170
  3. 2016Density estimation using Real NVPcited by 2,042
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  5. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  6. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  7. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  8. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  9. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  10. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  11. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  12. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
11 more
  1. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  2. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  3. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  4. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  5. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  6. 2020Explainable Deep One-Class Classificationcited by 127
  7. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  8. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  9. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  10. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  11. 2018Anomaly Detection using Deep Learning based Image Completioncited by 27

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 27; 2024: 69; 2025: 95; 2026: 65.

256 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. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  7. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  8. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 73
  9. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  10. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 63
  11. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  12. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
38 more
  1. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 48
  2. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  3. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  4. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  5. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detectioncited by 42
  6. 2023Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detectioncited by 39
  7. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  8. 2024Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generationcited by 32
  9. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  10. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 30
  11. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
  12. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 29
  13. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  14. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  15. 2025Towards Training-free Anomaly Detection with Vision and Language Foundation Modelscited by 26
  16. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  17. 2023An Incremental Unified Framework for Small Defect Inspectioncited by 25
  18. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  19. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  20. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  21. 2023GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detectioncited by 23
  22. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  23. 2023PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentationcited by 21
  24. 2024Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detectioncited by 19
  25. 2024Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detectioncited by 19
  26. 2025One-for-More: Continual Diffusion Model for Anomaly Detectioncited by 19
  27. 2025Learning to Detect Multi-class Anomalies with Just One Normal Image Promptcited by 18
  28. 2024Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detectioncited by 17
  29. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 16
  30. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  31. 2025MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learningcited by 13
  32. 2024SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detectioncited by 12
  33. 2024CSAD: Unsupervised Component Segmentation for Logical Anomaly Detectioncited by 12
  34. 2024AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localizationcited by 12
  35. 2025Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detectioncited by 11
  36. 2023SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classificationcited by 10
  37. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 10
  38. 2023Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localizationcited by 10

50 most cited of 256 All 256 (JSON)