mlsanityData to 14 Oct 2026

No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

arXiv 2508.19060 ↗First posted 26 Aug 2025cs.CV cs.AIcited by 4

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Abstract (arXiv)

Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components. While numerous methods have been proposed, many fail to meet industrial demands for high performance, efficiency, and adaptability. Existing approaches are often constrained to specific supervision scenarios and struggle to adapt to the diverse data annotations encountered in real-world manufacturing processes, such as unsupervised, weakly supervised, mixed supervision, and fully supervised settings. To address these challenges, we propose SuperSimpleNet, a highly efficient and adaptable discriminative model built on the foundation of SimpleNet. SuperSimpleNet incorporates a novel synthetic anomaly generation process, an enhanced classification head, and an improved learning procedure, enabling efficient training in all four supervision scenarios, making it the first model capable of fully leveraging all available data annotations. SuperSimpleNet sets a new standard for performance across all scenarios, as demonstrated by its results on four challenging benchmark datasets. Beyond accuracy, it is very fast, achieving an inference time below 10 ms. With its ability to unify diverse supervision paradigms while maintaining outstanding speed and reliability, SuperSimpleNet represents a promising step forward in addressing real-world manufacturing challenges and bridging the gap between academic research and industrial applications. Code: https://github.com/blaz-r/SuperSimpleNet

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 26 Aug 2025.

Builds on

30 of 50 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Wide Residual Networkscited by 3,070
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  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. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  7. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  8. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  9. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
  10. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  11. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  12. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
18 more
  1. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  2. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  3. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  4. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 154
  5. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  6. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  7. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  8. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  9. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  10. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  11. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  12. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  13. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  14. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  15. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  16. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  17. 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18
  18. 2020End-to-end training of a two-stage neural network for defect detectioncited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectorscited by 4
  2. 2025Explainable Visual Anomaly Detection via Concept Bottleneck Modelscited by 0
  3. 2026Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbationscited by 0
  4. 2026Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detectioncited by 0