A Survey on GANs for Anomaly Detection
A Survey on GANs for Anomaly Detection
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发表时间：
2019-06
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通讯作者：
Federico Di Mattia;P. Galeone;M. D. Simoni;Emanuele Ghelfi
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作者：
Federico Di Mattia;P. Galeone;M. D. Simoni;Emanuele Ghelfi
异常检测是多个研究领域面临的重要问题。检测和正确分类看不见的异常是一个具有挑战性的问题，多年来已经以许多不同的方式解决。生成对抗网络（GANs）和对抗训练过程最近被用来应对这一任务，并取得了显着的成果。在本文中，我们调查了主要的基于GAN的异常检测方法，突出了它们的优点和缺点。我们的贡献是对用于异常检测的主要GAN模型的实证验证，不同数据集上的实验结果的增加以及公开发布一个完整的使用GAN进行异常检测的开源工具箱。
Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. Generative Adversarial Networks (GANs) and the adversarial training process have been recently employed to face this task yielding remarkable results. In this paper we survey the principal GAN-based anomaly detection methods, highlighting their pros and cons. Our contributions are the empirical validation of the main GAN models for anomaly detection, the increase of the experimental results on different datasets and the public release of a complete Open Source toolbox for Anomaly Detection using GANs.