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	1	Model-based analysis of the image generation quality of adversarial latent autoencoders for industrial machine vision Yermakov, Ruslan. - Aachen : Universitätsbibliothek der RWTH Aachen, 2023
	2	Generating a New Reality Berkeley, CA : Apress, 2021, 1st edition
	3	A pore space reconstruction method of shale based on autoencoders and generative adversarial networks Enthalten in Computational geosciences 4.8.2021: 1-17
	4	Adversarial dual autoencoders for trust-aware recommendation Enthalten in Neural computing & applications 27.1.2021: 1-11
	5	A fusion of deep convolutional generative adversarial networks and sequence to sequence autoencoders for acoustic scene classification Amiriparian, Shahin. - Augsburg : Universität Augsburg, 2018
	6	Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders Miceli Barone, Antonio Valerio. - Aachen : Universitätsbibliothek der RWTH Aachen, 2016
	7	Adversarial and variational autoencoders improve metagenomic binning Enthalten in Communications biology Bd. 6, 21.10.2023, Nr. 1, date:12.2023: 1-10
	8	Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders Enthalten in European Association for Speech, Signal and Image Processing: EURASIP journal on advances in signal processing Bd. 2022, 22.11.2022, Nr. 1, date:12.2022: 1-13
	9	CH-Net: Deep adversarial autoencoders for semantic segmentation in X-ray images of cabin baggage screening at airports Enthalten in Journal of transportation security Bd. 13, 6.7.2020, Nr. 1-2, date:6.2020: 71-89
	10	Correction to: CH-Net: Deep adversarial autoencoders for semantic segmentation in X-ray images of cabin baggage screening at airports Enthalten in Journal of transportation security Bd. 13, 15.8.2020, Nr. 1-2, date:6.2020: 91

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