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Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines.
The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
Earlier work this paper cites.
Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
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Wasserstein gan, 2017
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Improved training of wasserstein gans, 2017
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks, 2017
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
Earlier work this paper cites.
Pennylane: Automatic differentiation of hybrid quantum-classical computations, 2018
Ville Bergholm, Josh Izaac, Maria Schuld, et al · 2018
Cited alongside, same era.
Quantum generative adversarial networks
Pierre-Luc Dallaire-Demers and Nathan Killoran · 2018
Cited alongside, same era.
Anomaly detection with generative adversarial networks, 2018
Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft · 2018
Cited alongside, same era.
Quantum generative adversarial learning
Seth Lloyd and Christian Weedbrook · 2018
Cited alongside, same era.
Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P. Breckon · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Quantum generative adversarial networks with multiple superconducting qubits
Kaixuan Huang, Zheng-An Wang, Chao Song, Kai Xu, Hekang Li, Zhen Wang, Qiujiang Guo, Zixuan Song, Zhi-Bo Liu, Dongning Zheng, et al · 2021
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Quantum semi-supervised generative adversarial network for enhanced data classification
Kouhei Nakaji and Naoki Yamamoto · 2021
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Qiskit: An open-source framework for quantum computing, 2021
A tA v, MD Sajis Anis, and Abby-Mitchell others · 2021
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Take a close look at mode collapse and vanishing gradient in gan
Zhitong Ding, Shuqi Jiang, and Jingya Zhao · 2022
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Generation of high-resolution handwritten digits with an ion-trap quantum computer
Manuel S Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa, Sonika Johri, Borja Peropadre, and Alejandro Perdomo-Ortiz · 2022
Later among the works it cites.
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Cited alongside, same era.
Experimental quantum generative adversarial networks for image generation
He-Liang Huang, Yuxuan Du, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, et al · 2021
Cited alongside, same era.
Shu Lok Tsang, Maxwell T West, Sarah M Erfani, and Muhammad Usman · 2022
Later among the works it cites.
Gan-based anomaly detection: A review
Xuan Xia, Xizhou Pan, Nan Li, Xing He, Lin Ma, Xiaoguang Zhang, and Ning Ding · 2022
Later among the works it cites.