"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."
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