mlsanityData to 14 Oct 2026

AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling

arXiv 2305.18389 ↗First posted 28 May 2023cs.LGcited by 0

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Abstract (arXiv)

Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. The main challenge is that in general we have access to very few labeled data or no labels at all. In this paper, we present a new semi-supervised anomaly detection method called AnoRand by combining a deep learning architecture with random synthetic label generation. The proposed architecture has two building blocks: (1) a noise detection (ND) block composed of feed forward ferceptron and (2) an autoencoder (AE) block. The main idea of this new architecture is to learn one class (e.g. the majority class in case of anomaly detection) as well as possible by taking advantage of the ability of auto encoders to represent data in a latent space and the ability of Feed Forward Perceptron (FFP) to learn one class when the data is highly imbalanced. First, we create synthetic anomalies by randomly disturbing (add noise) few samples (e.g. 2%) from the training set. Second, we use the normal and the synthetic samples as input to our model. We compared the performance of the proposed method to 17 state-of-the-art unsupervised anomaly detection method on synthetic datasets and 57 real-world datasets. Our results show that this new method generally outperforms most of the state-of-the-art methods and has the best performance (AUC ROC and AUC PR) on the vast majority of reference datasets. We also tested our method in a supervised way by using the actual labels to train the model. The results show that it has very good performance compared to most of state-of-the-art supervised algorithms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 28 May 2023.

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30 of 61 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  5. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  6. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  7. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  8. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  9. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  10. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  11. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  12. 2022ADBench: Anomaly Detection Benchmarkcited by 232
18 more
  1. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  2. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  3. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  4. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  5. 2019Deep Anomaly Detection with Deviation Networkscited by 153
  6. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  7. 2020COPOD: Copula-Based Outlier Detectioncited by 146
  8. 2018Adversarially Learned Anomaly Detectioncited by 115
  9. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  10. 2019Deep Weakly-supervised Anomaly Detectioncited by 59
  11. 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
  12. 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
  13. 2019XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learningcited by 42
  14. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  15. 2020Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noisecited by 39
  16. 2018One-Class Adversarial Nets for Fraud Detectioncited by 33
  17. 2022Perturbation Learning Based Anomaly Detectioncited by 24
  18. 2022SPADE: Semi-supervised Anomaly Detection under Distribution Mismatchcited by 9

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