GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Akçay, Samet; Atapour-Abarghouei, Amir; Breckon, Toby P.

doi:10.48550/arxiv.1805.06725

Cited by 21 publications

(57 citation statements)

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“…For unsupervised outlier detection and semisupervised outlier detection with only normal examples, GAN-based reconstruction model and generation model have been studied. GAN-based reconstruction models usually learn the generation mechanism of normal data by training a regular GAN [17] or a combination of GAN and autoencoder [18], [36], [37], and then measure the abnormal degree of example based on the reconstruction loss or discriminator loss. Moreover, in order to prevent slight anomalies from being reconstructed, Bian et al [38] also perform active negative training to limit network generative capability.…”

Section: Gan-based Outlier Detection Methodsmentioning

confidence: 99%

RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

Li¹,

Sun²,

Liu³

et al. 2020

Preprint

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Outlier detection is an important task in data mining and many technologies have been explored in various applications. However, due to the default assumption that outliers are non-concentrated, unsupervised outlier detection may not correctly detect group anomalies with higher density levels. As for the supervised outlier detection, although high detection rates and optimal parameters can usually be achieved, obtaining sufficient and correct labels is a time-consuming task. To address these issues, we focus on semi-supervised outlier detection with few identified anomalies, in the hope of using limited labels to achieve high detection accuracy. First, we propose a novel detection model Dual-GAN, which can directly utilize the potential information in identified anomalies to detect discrete outliers and partially identified group anomalies simultaneously. And then, considering the instances with similar output values may not all be similar in a complex data structure, we replace the two MO-GAN components in Dual-GAN with the combination of RCC and M-GAN (RCC-Dual-GAN). In addition, to deal with the evaluation of Nash equilibrium and the selection of optimal model, two evaluation indicators are created and introduced into the two models to make the detection process more intelligent. Extensive experiments on both benchmark datasets and two practical tasks demonstrate that our proposed approaches (i.e., Dual-GAN and RCC-Dual-GAN) can significantly improve the accuracy of outlier detection even with only a few identified anomalies. Moreover, compared with the two MO-GAN components in Dual-GAN, the network structure combining RCC and M-GAN has greater stability in various situations. The experiment codes are available at: https://github.com/leibinghe/RCC-Dual-GAN.

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Section: Gan-based Outlier Detection Methodsmentioning

confidence: 99%

RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

Li¹,

Sun²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…iii) Generation error (GE)-based methods [17,[40][41][42][43][44][45][46][47]. They first utilize GNN to learn the manifold distribution by minimizing the GE of normal samples, then utilize the GE to measure the deviation of the test samples from the distribution.…”

Section: Models For Anomaly Detectionmentioning

confidence: 99%

Main-Auxiliary Aggregation Strategy for Video Anomaly Detection

Wang¹,

Zhang²,

Wang³

et al. 2021

IEEE Signal Process. Lett.

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The aim of surveillance video anomaly detection is to detect events that rarely or never happened in a specified scene. Different detectors can detect different anomalies. This paper proposes an efficient strategy to aggregate multiple detectors together. At first, the aggregation strategy chooses one detector as master detector, and sets the other detectors as auxiliary detectors. Then, the aggregation strategy extracts credible information from auxiliary detectors, which includes credible abnormal (Cred-a) frames and credible normal (Cred-n) frames, and counts their Cred-a and Cred-n frequencies. Finally, the aggregation strategy utilizes the Cred-a and Cred-n frequencies to calculate soft weights in a voting manner, and uses the soft weights to assist the master detector. Experiments are carried out on multiple datasets. Compared with existing aggregation strategies, the proposed strategy achieves state-of-the-art performance.

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“…GANomaly learns the generation of the data and the inference of the latent space, z. It uses an encoder-decoderencoder in the generator and minimizing the distance between the vectors in z aids in learning the data distribution [14].…”

Section: A Gans Using a Reconstruction-based Anomaly Scorementioning

confidence: 99%

Tail of Distribution GAN (TailGAN): GenerativeAdversarial-Network-Based Boundary Formation

Dionelis

Yaghoobi

Tsaftaris

2020

2020 Sensor Signal Processing for Defence Conference (SSPD)

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Generative Adversarial Networks (GAN) are a powerful methodology and can be used for unsupervised anomaly detection, where current techniques have limitations such as the accurate detection of anomalies near the tail of a distribution. GANs generally do not guarantee the existence of a probability density and are susceptible to mode collapse, while few GANs use likelihood to reduce mode collapse. In this paper, we create a GAN-based tail formation model for anomaly detection, the Tail of distribution GAN (TailGAN), to generate samples on the tail of the data distribution and detect anomalies near the support boundary. Using TailGAN, we leverage GANs for anomaly detection and use maximum entropy regularization. Using GANs that learn the probability of the underlying distribution has advantages in improving the anomaly detection methodology by allowing us to devise a generator for boundary samples, and use this model to characterize anomalies. TailGAN addresses supports with disjoint components and achieves competitive performance on images. We evaluate TailGAN for identifying Out-of-Distribution (OoD) data and its performance evaluated on MNIST, CIFAR-10, Baggage X-Ray, and OoD data shows competitiveness compared to methods from the literature.

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GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Cited by 21 publications

References 0 publications

RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

Main-Auxiliary Aggregation Strategy for Video Anomaly Detection

Tail of Distribution GAN (TailGAN): GenerativeAdversarial-Network-Based Boundary Formation

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