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oai:www.ideals.illinois.edu:2142/110594 2023-07-11 col_2142_5131 col_2142_14800 com_2142_5130 com_2142_14799 com_2142_234

Lembeck, Michael F Tran, Huy T Dimon, Walker Lee 2021-09-17T01:13:33Z 2021-09-17T01:13:33Z 2021-04-28 2021-05 "Presented in this thesis is a novel Generative Adversarial Network, or GAN, based method, D-AnoGAN, for detecting anomalies in complex datasets containing disconnected data manifolds. Current state-of-the-art methods treat disconnected data manifolds as a single, continuous one to learn from. The key contribution of D-AnoGAN is specifically accounting for the discontinuity between manifolds within a dataset during training. To achieve this, a multi-generator network is first implemented, where each generator is responsible for learning a unique manifold of data. Second, a machine learning mechanism called a ''bandit"" is implemented to find the optimal set of generators required to cover all data manifolds through unsupervised prior-learning. Finally, the multi-generator and bandit are used to cluster data from the same manifold together during training, allowing them to be learned in a disconnected fashion. The proposed method's effectiveness is demonstrated on two publicly available datasets, as well as a new experimental dataset developed in-house, where state-of-the-art results are achieved." Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms The student, Walker Dimon, accepted the attached license on 2021-04-28 at 12:00. The student, Walker Dimon, submitted this Thesis for approval on 2021-04-28 at 12:11. This Thesis was approved for publication on 2021-04-28 at 14:59. DSpace SAF Submission Ingestion Package generated from Vireo submission #16602 on 2021-09-16 at 16:49:20 Made available in DSpace on 2021-09-17T01:13:33Z (GMT). No. of bitstreams: 3 DIMON-THESIS-2021.pdf: 3148125 bytes, checksum: d3bc6e7f38d2f2ad7a39fa5522797d3d (MD5) DIMON-THESIS-LATEX-FILES.zip: 3084895 bytes, checksum: 66a83e5b7dfe9045f24fa040e9eb6c17 (MD5) LICENSE.txt: 4209 bytes, checksum: 991245f6df61b80c605d4fcc37ae04c0 (MD5) Previous issue date: 2021-04-28 application/pdf http://hdl.handle.net/2142/110594 en Copyright 2021 Walker Dimon Generative Adversarial Networks, Unsupervised Anomaly Detection, Computer Vision, Unsupervised Learning, Machine Learning Unsupervised anomaly detection in multi-class datasets using Generative Adversarial Networks text Thesis Aerospace Engineering Aerospace Engineering University of Illinois at Urbana-Champaign Thesis M.S.