Anomaly Detection via Minimum Likelihood Generative Adversarial Networks
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Abstract (arXiv)
Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection has become more and more popular. In this paper, a new domain-based anomaly detection method based on generative adversarial networks (GAN) is proposed. Minimum likelihood regularization is proposed to make the generator produce more anomalies and prevent it from converging to normal data distribution. Proper ensemble of anomaly scores is shown to improve the stability of discriminator effectively. The proposed method has achieved significant improvement than other anomaly detection methods on Cifar10 and UCI datasets.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2018.
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Citing papers per year: 2021: 4; 2022: 4; 2023: 2.
10 papers.
- 2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
- 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
- 2023FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networkscited by 8
- 2022Raising the Bar on the Evaluation of Out-of-Distribution Detectioncited by 7
- 2021Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundarycited by 6
- 2021Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formationcited by 6
- 2021OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundarycited by 2
- 2022Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detectioncited by 2
- 2022Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspectionscited by 0
- 2023Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scorescited by 0
- 2023Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores27 Oct 2023
- 2023FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks17 Jan 2023
- 2022Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections22 Dec 2022
- 2022Raising the Bar on the Evaluation of Out-of-Distribution Detection24 Sep 2022
- 2022Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection5 Apr 2022
- 2022Machine Learning in NextG Networks via Generative Adversarial Networks9 Mar 2022
- 2021OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary28 Oct 2021
- 2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review22 Oct 2021
- 2021Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation24 Jul 2021
- 2021Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary21 Jul 2021