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Unsupervised learning of anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research.
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2016
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Peter Christiansen, Lars N Nielsen, Kim A Steen, Rasmus N Jørgensen, and Henrik Karstoft, ‘DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field.’, Sensors (Basel, Switzerland)
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Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft, ‘Image Anomaly Detection with Generative Adversarial Networks’, in Machine Learning and Knowledge Discovery in Databases
2018
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2016
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow, ‘Adversarial Autoencoders’, in International Conference on Learning Representations
2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou, ‘Wasserstein GAN’, CoRR
2017
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2017
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Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville, ‘Improved Training of Wasserstein GANs’, in NIPS’17 Proceedings of the 31st International Conference on Neural Information Processing Systems
2017
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Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen, ‘Progressive Growing of GANs for Improved Quality, Stability, and Variation’, in ICLR 2018
2017
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Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, and Kuinam J. Kim, ‘A Survey of Deep Learning-Based Network Anomaly Detection’, Cluster Computing
2017
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Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs, ‘Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery’, in Information Processing in Medical Imaging
2017
Cited alongside, same era.
2018
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Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, and Vijay Ramaseshan Chandrasekhar, ‘Adversarially Learned Anomaly Detection’, in 2018 IEEE International Conference on Data Mining (ICDM)
2018
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Samet Akçay, Amir Atapour-Abarghouei, and Toby P. Breckon, ‘GANomaly: Semi-supervised Anomaly Detection via Adversarial Training’, in 2018 Asian Conference on Computer Vision (ACCV)
2019
Closest in time.
Samet Akçay, Amir Atapour-Abarghouei, and Toby P. Breckon, ‘Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection’, in 2019 International Joint Conference on Neural Networks (IJCNN)
2019
Closest in time.
2019
Closest in time.
Raghavendra Chalapathy and Sanjay Chawla, ‘Deep Learning for Anomaly Detection: A Survey’, CoRR
2019
Closest in time.
2019
Closest in time.
Thomas Schlegl, Philipp Seeböck, Ursula Schmidt-Erfurth, Georg Langs, Thomas Schlegl, and Sebastian M. Waldstein, ‘f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks’, Medical Image Analysis
2019
Closest in time.