A Primer on Generative Adversarial Networks
Keywords:
Deep Learning, Generative Adversarial Networks, Neural Network, ApplicationAbstract
Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs. This will take you through how every part function and the significant ideas and innovation behind GANs. It will likewise give a short outline of the advantages and downsides of utilizing GANs, comparison of architectures of various GANs and knowledge into certain true applications.
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References
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas(19 Oct 2017 ). StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Alec Radford, Luke Metz, Soumith Chintala (Submitted on 19 Nov 2015 (v1)). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Jonathan Shapiro (April 2016). Text to Image Synthesis Using Generative Adversarial Networks [4] Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao: MirrorGAN: Learning Text-to- image Generation by Redescription ( 14 Mar 2019)
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde- Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio: Generative Adversarial Networks( 10 Jun 2014)
https://www.slideshare.net/xavigiro/deep-learning-f or-computer-vision-generative-models-and-adversa rial-training-upc-2016
https://www.geeksforgeeks.com/Generative-adhersar ial-networks
(Ahirwar, 2019). Generative Adversarial Networks Projects
(Google, n.d.) https://developers.google.com/machine-learning/gan [10] (htt).https://medium.com/@rangerscience/lets-read -science-stackgan-text-to-photo-realistic-image-syn thesis-4562b2b14059
Augustus Odena, Christopher Olah, Jonathon Shlens: Conditional Image Synthesis With Auxiliary Classifier GANs (20 July 2017)
Andrew Brock, Jeff Donahue, Karen Simonyan: Large Scale GAN Training for High Fidelity Natural Image Synthesis (25 Feb 2019)
https://arxiv.org/pdf/1508.06576.pdf
Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (15 Nov 2018)
https://arxiv.org/pdf/1910.00927.pdf
https://subscription.packtpub.com/book/big_data_a nd_business_intelligence/9781789136678/2/ch02lv l1sec18/introduction-to-3d-gans
Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus: Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks (18 Jun 2015)
https://www.researchgate.net/publication/32691485 6_Review_on_Generative_Adversarial_Networks [19] https://link.springer.com/article/10.1007/s11831-0 19-09388-y/tables/1
