mlsanityData to 7 Oct 2026

ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection

arXiv 2407.01312 ↗First posted 1 Jul 2024cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

Current unsupervised anomaly detection approaches perform well on public datasets but struggle with specific anomaly types due to the domain gap between pre-trained feature extractors and target-specific domains. To tackle this issue, this paper presents a two-stage training strategy, called ToCoAD. In the first stage, a discriminative network is trained by using synthetic anomalies in a self-supervised learning manner. This network is then utilized in the second stage to provide a negative feature guide, aiding in the training of the feature extractor through bootstrap contrastive learning. This approach enables the model to progressively learn the distribution of anomalies specific to industrial datasets, effectively enhancing its generalizability to various types of anomalies. Extensive experiments are conducted to demonstrate the effectiveness of our proposed two-stage training strategy, and our model produces competitive performance, achieving pixel-level AUROC scores of 98.21%, 98.43% and 97.70% on MVTec AD, VisA and BTAD respectively.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 47 of its 71 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

47 of 71 references.

References · 47

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  8. 2016Wide Residual Networkscited by 3,057
  9. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  10. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  11. 2015Variational Inference with Normalizing Flowscited by 2,161
  12. 2013Describing Textures in the Wildcited by 2,110
35 more
  1. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  3. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  5. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  6. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  7. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  8. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  9. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  10. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  11. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  12. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  13. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  14. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  15. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  16. 2020Adversarial Self-Supervised Contrastive Learningcited by 138
  17. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  18. 2020Contrastive Learning with Adversarial Examplescited by 91
  19. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  20. 2021Cross-domain Contrastive Learning for Unsupervised Domain Adaptationcited by 75
  21. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  22. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  23. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  24. 2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
  25. 2022How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learningcited by 65
  26. 2021Unpaired Deep Image Deraining Using Dual Contrastive Learningcited by 60
  27. 2022Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learningcited by 53
  28. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  29. 2021Contrastive Domain Adaptationcited by 36
  30. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  31. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 22
  32. 2022FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detectioncited by 16
  33. 2022GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grainscited by 9
  34. 2022Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regimecited by 5
  35. 2023A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Imagescited by 5

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 5.

7 papers.

  1. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  2. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  3. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  4. 2025ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defectscited by 2
  5. 2025CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Mediacited by 2
  6. 2025IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detectioncited by 2
  7. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categoriescited by 2