mlsanityData to 7 Oct 2026

SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

arXiv 2408.03143 ↗First posted 6 Aug 2024cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of these industries, which encompass high performance, consistency, and fast operation, along with the capacity to leverage the entirety of the available training data. Addressing these gaps, we introduce SuperSimpleNet, an innovative discriminative model that evolved from SimpleNet. This advanced model significantly enhances its predecessor's training consistency, inference time, as well as detection performance. SuperSimpleNet operates in an unsupervised manner using only normal training images but also benefits from labelled abnormal training images when they are available. SuperSimpleNet achieves state-of-the-art results in both the supervised and the unsupervised settings, as demonstrated by experiments across four challenging benchmark datasets. Code: https://github.com/blaz-r/SuperSimpleNet .

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 22 of its 29 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

22 of 29 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Wide Residual Networkscited by 3,057
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  4. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  5. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  6. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  7. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  8. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  9. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  10. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  11. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  12. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
10 more
  1. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  2. 2020Explainable Deep One-Class Classificationcited by 126
  3. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  4. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  5. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  6. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  7. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  8. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  9. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  10. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 7.

12 papers.

  1. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  2. 2025Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detectioncited by 7
  3. 2025INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learningcited by 7
  4. 2025Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practicescited by 6
  5. 2025MoViAD: A Modular Library for Visual Anomaly Detectioncited by 0
  6. 2026Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbationscited by 0
  7. 2026VAD4Space: Visual Anomaly Detection for Planetary Surface Imagerycited by 0
  8. 2026AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Drivingcited by 0
  9. 2026Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detectioncited by 0
  10. 2026LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturingcited by 0
  11. 2026From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detectioncited by 0
  12. 2026CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentationcited by 0