ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables

Abstract

In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowledge, they may be subject to user bias. In this paper, we propose an Anomalous Latent variable Generative Adversarial Network (ALGAN) in which the GAN generator produces pseudo-anomalous data as well as fake-normal data, whereas the discriminator is trained to distinguish between normal and pseudo-anomalous data. This differs from the standard GAN discriminator, which specializes in classifying two similar classes. The training dataset contains only normal data; the latent variables are introduced in anomalous states and are input into the generator to produce diverse pseudo-anomalous data. We compared the performance of ALGAN with other existing methods on the MVTec-AD, Magnetic Tile Defects, and COIL-100 datasets. The experimental results showed that ALGAN exhibited an AUROC comparable to those of state-of-the-art methods while achieving a much faster prediction time.

arXiv comment13 pages, 8 figures

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Scikit-learn: Machine Learning in Python
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Spectral Normalization for Generative Adversarial Networks
• Deep Learning for Anomaly Detection: A Review
• Deep Learning for Anomaly Detection: A Survey
• A Unifying Review of Deep and Shallow Anomaly Detection
• Active Learning for Convolutional Neural Networks: A Core-Set Approach
• Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
• Efficient GAN-Based Anomaly Detection
• Sub-Image Anomaly Detection with Deep Pyramid Correspondences
• A survey on text generation using generative adversarial networks
• Deep Nearest Neighbor Anomaly Detection
• Generative adversarial networks in time series: A survey and taxonomy
• Small-GAN: Speeding Up GAN Training Using Core-sets

Cited by in corpus (1)

• PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies