mlsanityData to 14 Oct 2026

Fine-grained Anomaly Detection via Multi-task Self-Supervision

arXiv 2104.09993 ↗First posted 20 Apr 2021cs.CVcited by 2

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Abstract (arXiv)

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining in a multi-task framework high-scale shape features oriented task with low-scale fine features oriented task, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

25 of 43 references.

References · 25

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2016Wide Residual Networkscited by 3,070
  4. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  5. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
  6. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  7. 2016Colorful Image Colorizationcited by 1,114
  8. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  9. 2016Conditional Image Generation with PixelCNN Decoderscited by 862
  10. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  11. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  12. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
13 more
  1. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  2. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  3. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  4. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  5. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  6. 2019Deep Anomaly Detection with Deviation Networkscited by 153
  7. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  8. 2020DROCC: Deep Robust One-Class Classificationcited by 121
  9. 2019A critical analysis of self-supervision, or what we can learn from a single imagecited by 76
  10. 2019Deep Tree Learning for Zero-shot Face Anti-Spoofingcited by 73
  11. 2018Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detectioncited by 46
  12. 2020Puzzle-AE: Novelty Detection in Images through Solving Puzzlescited by 23
  13. 2020Video Anomaly Detection for Smart Surveillancecited by 16

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Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.

  1. 2021Efficient Anomaly Detection Using Self-Supervised Multi-Cue Taskscited by 6
  2. 2022Anomaly Detection via Multi-Scale Contrasted Memorycited by 0