Adversarially Learned Anomaly Detection

Zenati, Houssam; Romain, Manon; Foo, Chuan-Sheng; Lecouat, Bruno; Chandrasekhar, Vijay

doi:10.1109/icdm.2018.00088

Cited by 325 publications

(279 citation statements)

References 14 publications

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“…Existing deep anomaly detection 1 methods [2,7,19,20,22,29,30] address these two challenges by using unsupervised deep learning to model the normal class in a two-step approach (i.e., the pipeline (a) in Figure 1): they first learn to represent data with new representations, e.g., intermediate representations in autoencoders [2,7,30], latent spaces in generative adversarial networks (GANs) [22,29], or distance metric spaces in [19,20]; and then they use the learned representations to define anomaly scores using reconstruction errors [2,7,22,29,30] or distance-based measures [19,20]. However, in most of these methods [2,7,22,29,30], the representation learning is separate from anomaly detection methods, so it may yield representations that are suboptimal or even irrelevant w.r.t. specific anomaly detection methods.…”

Section: Introductionmentioning

confidence: 99%

Deep Anomaly Detection with Deviation Networks

Pang

Shen

Hengel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

261

168

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Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection applications.This paper introduces a novel anomaly detection framework and its instantiation to address these problems. Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail. Extensive results show that our method can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods.

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Section: Introductionmentioning

confidence: 99%

Deep Anomaly Detection with Deviation Networks

Pang

Shen

Hengel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

261

168

View full text Add to dashboard Cite

show abstract

“…GANs [6] created a new branch in the development of image anomaly detection. GAN-based approaches [7][8][9][10][11] differ in two parts: (i) how to find latent vectors that correspond to the input images, (ii) how to estimate abnormality based Figure 2: Comparison of four anomaly detection models. G denotes the generator, E the encoder, D * the discriminators, "rec.…”

Section: Related Workmentioning

confidence: 99%

“…For ADGAN [8], OCGAN [9], ALAD [11], GPND [20], LSA [21] we used results as reported in the corresponding publications. Results for OC-SVM, KDE, AnoGAN were obtained from [21].…”

Section: Algorithm 3 Select Weighting Parametermentioning

confidence: 99%

Perceptual Image Anomaly Detection

Tuluptceva¹,

Bakker²,

Fedulova³

et al. 2020

Lecture Notes in Computer Science

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We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of "normal" data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and generator for mapping an image distribution to a predefined latent distribution and vice versa. It leverages Generative Adversarial Networks to learn these data distributions and uses perceptual loss for the detection of image abnormality. To accomplish this goal, we introduce a new similarity metric, which expresses the perceived similarity between images and is robust to changes in image contrast. Secondly, we introduce a novel approach for the selection of weights of a multi-objective loss function (image reconstruction and distribution mapping) in the absence of a validation dataset for hyperparameter tuning. After training, our model measures the abnormality of the input image as the perceptual dissimilarity between it and the closest generated image of the modeled data distribution. The proposed approach is extensively evaluated on several publicly available image benchmarks and achieves state-of-the-art performance.

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“…After training, the generator produces examples from the distribution of the original data. The discriminator, whereas, can detect novelties and outliers in the data [20]. Often, GANs employ the Wasserstein distance [21] or the binary cross entropy (BCE) [22] as loss function.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic

Schreiber

Jessulat

Sick

2019

Lecture Notes in Computer Science

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For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Recently, research in scenario planning benefits from utilizing generative adversarial networks (GANs) as generative models for operational scenario planning. In these scenarios, operators examine temporal as well as spatial influences of different energy sources on the grid. The analysis of how renewable energy resources affect the grid enables the operators to evaluate the stability and to identify potential weak points such as a limiting transformer. However, due to their novelty, there are limited studies on how well GANs model the underlying power distribution. This analysis is essential because, e.g., especially extreme situations with low or high power generation are required to evaluate grid stability. We conduct a comparative study of the Wasserstein distance, binary-cross-entropy loss, and a Gaussian copula as the baseline applied on two wind and two solar datasets with limited data compared to previous studies. Both GANs achieve good results considering the limited amount of data, but the Wasserstein GAN is superior in modeling temporal and spatial relations, and the power distribution. Besides evaluating the generated power distribution over all farms, it is essential to assess terrain specific distributions for wind scenarios. These terrain specific power distributions affect the grid by their differences in their generating power magnitude. Therefore, in a second study, we show that even when simultaneously learning distributions from wind parks with terrain specific patterns, GANs are capable of modeling these individualities also when faced with limited data. These results motivate a further usage of GANs as generative models in scenario planning as well as other areas of renewable energy.

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Adversarially Learned Anomaly Detection

Cited by 325 publications

References 14 publications

Deep Anomaly Detection with Deviation Networks

Deep Anomaly Detection with Deviation Networks

Perceptual Image Anomaly Detection

Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic

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