{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:34:31Z","timestamp":1730266471289,"version":"3.28.0"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892896","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T15:56:04Z","timestamp":1664553364000},"page":"1-8","source":"Crossref","is-referenced-by-count":5,"title":["Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators"],"prefix":"10.1109","author":[{"given":"Jan-Philipp","family":"Schulze","sequence":"first","affiliation":[{"name":"Technical University of Munich,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Sperl","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin","family":"Bottinger","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","author":"xiao","year":"2017","journal-title":"arXiv 1708 07747 [cs stat]"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref33","first-page":"214","article-title":"Wasserstein Generative Adversarial Networks","volume":"70","author":"arjovsky","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref32","first-page":"449","article-title":"The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise","volume":"33","author":"diakonikolas","year":"2020","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1109\/ICTAI.2019.00028"},{"key":"ref30","first-page":"365","article-title":"Unsupervised anomaly detection with adversarial mirrored autoencoders","author":"somepalli","year":"0","journal-title":"Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence"},{"doi-asserted-by":"publisher","key":"ref37","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00026"},{"doi-asserted-by":"publisher","key":"ref36","DOI":"10.1016\/S0168-1699(99)00046-0"},{"key":"ref35","article-title":"UCI Machine Learning Repository","author":"dua","year":"2017","journal-title":"University of California Irvine School of Information and Computer Sciences"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1109\/SSCI.2015.33"},{"key":"ref28","article-title":"Classification-Based Anomaly Detection for General Data","author":"bergman","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref27","article-title":"Deep Anomaly Detection with Outlier Exposure","author":"hendrycks","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref29","first-page":"9016","article-title":"Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure","author":"sipple","year":"0","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1145\/3292500.3330871"},{"key":"ref1","article-title":"Deep Semi-Supervised Anomaly Detection","author":"ruff","year":"0","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1007\/978-3-030-46150-8_13"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/ICDM.2018.00146"},{"key":"ref21","first-page":"8764","article-title":"A General Framework For Detecting Anomalous Inputs to DNN Classifiers","author":"raghuram","year":"0","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1609\/aaai.v35i8.16834"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1109\/UEMCON.2018.8796769"},{"key":"ref26","article-title":"Deep Anomaly Detection Using Geometric Transformations","volume":"31","author":"golan","year":"2018","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1609\/aaai.v33i01.33011286"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/TKDE.2021.3118815"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1609\/aaai.v33i01.33019428"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref12","article-title":"Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection","author":"zong","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","article-title":"Unsupervised Anomaly Detection with Generative Adversar-ial Networks to Guide Marker Discovery","author":"schlegl","year":"2017","journal-title":"Information Processing in Medical Imaging"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1016\/j.media.2019.01.010"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/ICDM.2018.00088"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref17","first-page":"622","article-title":"GANomaly: Semi-supervised Anomaly Detection via Adversarial Training","author":"akcay","year":"2019","journal-title":"Computer Vision - ACCV 2018"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1609\/aaai.v35i5.16530"},{"key":"ref19","article-title":"Anomaly Detection with Adversarial Dual Autoencoders","author":"vu","year":"2019","journal-title":"arXiv 1902 06924"},{"key":"ref4","first-page":"2672","article-title":"Generative Adversarial Nets","author":"goodfellow","year":"2014","journal-title":"Advances in Neural Information Processing Systems 27"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1007\/978-3-030-67661-2_5"},{"key":"ref6","first-page":"582","article-title":"Support vector method for novelty detection","author":"sch\u00f6lkopf","year":"2000","journal-title":"Advances in neural information processing systems"},{"key":"ref5","article-title":"Adversarial Au-toencders","author":"makhzani","year":"0","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1145\/3439950"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref49","first-page":"4502","author":"bau","year":"2019","journal-title":"Seeing what a gan cannot generate"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/JPROC.2021.3052449"},{"doi-asserted-by":"publisher","key":"ref46","DOI":"10.1145\/3219819.3220042"},{"key":"ref45","first-page":"4393","article-title":"Deep One-Class Classification","author":"ruff","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1007\/978-1-4612-4380-9_16","article-title":"Individual Comparisons by Ranking Methods","author":"wilcoxon","year":"1992","journal-title":"Break-throughs in Statistics Methodology and Distribution ser Springer Series in Statistics"},{"key":"ref47","article-title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","author":"hendrycks","year":"0","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1142\/S0218001493000698"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.1109\/5.726791"},{"key":"ref44","article-title":"Adam: A Method for Stochastic Optimization","author":"kingma","year":"2017","journal-title":"arXiv 1412 6980"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1007\/978-3-319-46298-1_30"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2022,7,18]]},"location":"Padua, Italy","end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892896.pdf?arnumber=9892896","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T18:56:36Z","timestamp":1667501796000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892896\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892896","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}