{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fence-gan-towards-better-anomaly-detection","title":"Fence GAN: Towards Better Anomaly Detection","arxiv_id":"1904.01209","date":"2019-04-02","proceeding":null,"authors":["Cuong Phuc Ngo","Amadeus Aristo Winarto","Connie Kou Khor Li","Sojeong Park","Farhan Akram","Hwee Kuan Lee"],"abstract":"Anomaly detection is a classical problem where the aim is to detect anomalous\ndata that do not belong to the normal data distribution. Current\nstate-of-the-art methods for anomaly detection on complex high-dimensional data\nare based on the generative adversarial network (GAN). However, the traditional\nGAN loss is not directly aligned with the anomaly detection objective: it\nencourages the distribution of the generated samples to overlap with the real\ndata and so the resulting discriminator has been found to be ineffective as an\nanomaly detector. In this paper, we propose simple modifications to the GAN\nloss such that the generated samples lie at the boundary of the real data\ndistribution. With our modified GAN loss, our anomaly detection method, called\nFence GAN (FGAN), directly uses the discriminator score as an anomaly\nthreshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets\nshow that Fence GAN yields the best anomaly classification accuracy compared to\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1904.01209v1","url_pdf":"http://arxiv.org/pdf/1904.01209v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fence-gan-towards-better-anomaly-detection","repo_url":"https://github.com/phuccuongngo99/Fence_GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-classification","task_name":"Anomaly Classification"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.01209","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}