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Generative Adversarial Networks (GANs) have been widely applied in different scenarios thanks to the development of deep neural networks.
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Towards Generalized Implementation of Wasserstein Distance in GANs
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From Knothe’s rearrangement to Brenier’s optimal transport map
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Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
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Generative adversarial nets. In Advances in Neural Information Processing Systems . 2672–2680
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Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision . 1422–1430
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Neural photo editing with introspective adversarial networks
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Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 770–778
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f-gan: Training generative neural samplers using variational divergence minimization. In Advances in Neural Information Processing Systems . 271–279
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Improved techniques for training gans. In Advances in Neural Information Processing Systems . 2234–2242
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In Advances in Neural Information Processing Systems . 901–909
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016 · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou. 2017a · 2017
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Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor. 2017 · 2017
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow. 2017 · 2017
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Improved training of wasserstein gans. In Advances in Neural Information Processing Systems . 5767–5777
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017 · 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 · 2017
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Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2017 · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE International Conference on Computer Vision . 1501–1510
Xun Huang and Serge Belongie. 2017 · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017 · 2017
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On convergence and stability of gans
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. 2017 · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2017 · 2017
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Regularization for deep learning: A taxonomy
Jan Kukačka, Vladimir Golkov, and Daniel Cremers. 2017 · 2017
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On the limitations of first-order approximation in gan dynamics
Jerry Li, Aleksander Madry, John Peebles, and Ludwig Schmidt. 2017 · 2017
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Jae Hyun Lim and Jong Chul Ye. 2017 · 2017
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Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision . 2794–2802
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley. 2017 · 2017
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The numerics of gans. In Advances in Neural Information Processing Systems . 1825–1835
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017 · 2017
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Mcgan: Mean and covariance feature matching gan
Youssef Mroueh, Tom Sercu, and Vaibhava Goel. 2017 · 2017
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Gradient descent GAN optimization is locally stable. In Advances in Neural Information Processing Systems . 5585–5595
Vaishnavh Nagarajan and J Zico Kolter. 2017 · 2017
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On the regularization of wasserstein gans
Henning Petzka, Asja Fischer, and Denis Lukovnicov. 2017 · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning. In Advances in Neural Information Processing Systems . 3308–3318
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton. 2017 · 2017
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Tag disentangled generative adversarial networks for object image re-rendering. In International joint conference on artificial intelligence (IJCAI)
Chaoyue Wang, Chaohui Wang, Chang Xu, and Dacheng Tao. 2017 · 2017
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On the effects of batch and weight normalization in generative adversarial networks
Sitao Xiang and Hao Li. 2017 · 2017
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Stabilizing adversarial nets with prediction methods
Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, and Tom Goldstein. 2017 · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017a · 2017
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Banach wasserstein gan. In Advances in Neural Information Processing Systems . 6754–6763
Jonas Adler and Sebastian Lunz. 2018 · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018 · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition . 8789–8797
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2018 · 2018
Cited alongside, same era.
Patch-based image inpainting with generative adversarial networks
Ugur Demir and Gozde Unal. 2018 · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018 · 2018
Cited alongside, same era.
A variational inequality perspective on generative adversarial networks
Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, and Simon Lacoste-Julien. 2018 · 2018
Cited alongside, same era.
Spectral bounding: Strictly satisfying the 1-Lipschitz property for generative adversarial networks
Zhihong Zhang, Yangbin Zeng, Lu Bai, Yiqun Hu, Meihong Wu, Shuai Wang, and Edwin R Hancock. 2019a · 2019
Later among the works it cites.
Lipschitz generative adversarial nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, and Zhihua Zhang. 2019a · 2019
Later among the works it cites.
DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning
Gulcin Baykal and Gozde Unal. 2020 · 2020
Closest in time.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. 2020a · 2020
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SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains
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Image-to-image translation for cross-domain disentanglement. In Advances in Neural Information Processing Systems . 1287–1298
Abel Gonzalez-Garcia, Joost Van De Weijer, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
The relativistic discriminator: a key element missing from standard GAN
Alexia Jolicoeur-Martineau. 2018 · 2018
Cited alongside, same era.
The gan landscape: Losses, architectures, regularization, and normalization
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly. 2018a · 2018
Cited alongside, same era.
A large-scale study on regularization and normalization in GANs
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly. 2018b · 2018
Cited alongside, same era.
Which training methods for GANs do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. 2018 · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018a · 2018
Cited alongside, same era.
cGANs with projection discriminator
Takeru Miyato and Masanori Koyama. 2018 · 2018
Cited alongside, same era.
Yuanqi Chen, Ge Li, Cece Jin, Shan Liu, and Thomas Li. 2020c · 2020
Closest in time.
Stargan v2: Diverse image synthesis for multiple domains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8188–8197
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha. 2020 · 2020
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Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 7890–7899
Ricard Durall, Margret Keuper, and Janis Keuper. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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Low Rank Regularization: A review
Zhanxuan Hu, Feiping Nie, Rong Wang, and Xuelong Li. 2020 · 2020
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FX-GAN: Self-Supervised GAN Learning via Feature Exchange. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision . 3194–3202
Rui Huang, Wenju Xu, Teng-Yok Lee, Anoop Cherian, Ye Wang, and Tim Marks. 2020 · 2020
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Msg-gan: Multi-scale gradients for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 7799–7808
Animesh Karnewar and Oliver Wang. 2020 · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2020 · 2020
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Generative adversarial networks and its applications in biomedical informatics
Lan Lan, Lei You, Zeyang Zhang, Zhiwei Fan, Weiling Zhao, Nianyin Zeng, Yidong Chen, and Xiaobo Zhou. 2020 · 2020
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Drit++: Diverse image-to-image translation via disentangled representations
Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, and Ming-Hsuan Yang. 2020b · 2020
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Kwot Sin Lee, Ngoc-Trung Tran, and Ngai-Man Cheung. 2020a · 2020
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Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies
Minhyeok Lee and Junhee Seok. 2020 · 2020
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Towards faster and stabilized gan training for high-fidelity few-shot image synthesis. In International Conference on Learning Representations
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. 2020 · 2020
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Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation
Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, and Nakamasa Inoue. 2020 · 2020
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Loss-sensitive generative adversarial networks on lipschitz densities
Guo-Jun Qi. 2020 · 2020
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Training generative adversarial networks by solving ordinary differential equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, and Pushmeet Kohli. 2020 · 2020
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Encoding in style: a stylegan encoder for image-to-image translation
Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. 2020 · 2020
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Xgan: Unsupervised image-to-image translation for many-to-many mappings
Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, and Kevin Murphy. 2020 · 2020
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Interpreting the latent space of gans for semantic face editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9243–9252
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou. 2020 · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel. 2020 · 2020
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Towards Good Practices for Data Augmentation in GAN Training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung. 2020 · 2020
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Attentive Normalization for Conditional Image Generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5094–5103
Yi Wang, Ying-Cong Chen, Xiangyu Zhang, Jian Sun, and Jiaya Jia. 2020 · 2020
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Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P Xing, and Zhiting Hu. 2020 · 2020
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Real or not real, that is the question
Yuanbo Xiangli, Yubin Deng, Bo Dai, Chen Change Loy, and Dahua Lin. 2020 · 2020
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Empirical Analysis of Overfitting and Mode Drop in GAN Training. In 2020 IEEE International Conference on Image Processing (ICIP) . IEEE, 1651–1655
Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, and Vijay Chandrasekhar. 2020 · 2020
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Semi-supervised broad learning system based on manifold regularization and broad network
Huimin Zhao, Jianjie Zheng, Wu Deng, and Yingjie Song. 2020d · 2020
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han. 2020a · 2020
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Improved consistency regularization for gans
Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, and Han Zhang. 2020b · 2020
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Image Augmentations for GAN Training
Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang. 2020c · 2020
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A survey on text generation using generative adversarial networks
Gustavo H de Rosa and João P Papa. 2021 · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
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Training GANs with Stronger Augmentations via Contrastive Discriminator
Jongheon Jeong and Jinwoo Shin. 2021 · 2021
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Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy. 2021 · 2021
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Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation. In 2020 25th International Conference on Pattern Recognition (ICPR) . 1942–1948
Ziqiang Li, Rentuo Tao, Hongjing Niu, Mingdao Yue, and Bin Li. 2021a · 2021
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Interpreting the Latent Space of GANs via Measuring Decoupling
Ziqiang Li, Rentuo Tao, Jie Wang, Fu Li, Hongjing Niu, Mingdao Yue, and Bin Li. 2021b · 2021
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Are high-frequency components beneficial for training of generative adversarial networks
Ziqiang Li, Pengfei Xia, Xue Rui, Yanghui Hu, and Bin Li. 2021c · 2021
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LT-GAN: Self-Supervised GAN with Latent Transformation Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 3189–3198
Parth Patel, Nupur Kumari, Mayank Singh, and Balaji Krishnamurthy. 2021 · 2021
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Medical image generation using generative adversarial networks: A review
Nripendra Kumar Singh and Khalid Raza. 2021 · 2021
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Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, and Carola-Bibiane Schonlieb. 2021 · 2021
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Generalization of GANs under Lipschitz continuity and data augmentation
Khoat Than and Nghia Vu. 2021 · 2021
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Regularizing Generative Adversarial Networks under Limited Data
Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, and Weilong Yang. 2021 · 2021
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View Vertically: A hierarchical network for trajectory prediction via fourier spectrums
Conghao Wong, Beihao Xia, Ziming Hong, Qinmu Peng, Wei Yuan, Qiong Cao, Yibo Yang, and Xinge You. 2021 · 2021
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Gradient normalization for generative adversarial networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 6373–6382
Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam, and Hong-Yu Chiu. 2021 · 2021
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Data-efficient instance generation from instance discrimination
Ceyuan Yang, Yujun Shen, Yinghao Xu, and Bolei Zhou. 2021 · 2021
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Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I Chang, and Yan Xu. 2021 · 2021
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GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 3821–3830
Vineeth S Bhaskara, Tristan Aumentado-Armstrong, Allan D Jepson, and Alex Levinshtein. 2022 · 2022
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Generative adversarial networks for spatio-temporal data: A survey
Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D Salim. 2022 · 2022
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FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Ziqiang Li, Chaoyue Wang, Heliang Zheng, Jing Zhang, and Bin Li. 2022a · 2022
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A Comprehensive Survey on Data-Efficient GANs in Image Generation
Ziqiang Li, Xintian Wu, Beihao Xia, Jing Zhang, Chaoyue Wang, and Bin Li. 2022b · 2022
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A New Perspective on Stabilizing GANs Training: Direct Adversarial Training
Ziqiang Li, Pengfei Xia, Rentuo Tao, Hongjing Niu, and Bin Li. 2022c · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings . 1–10
Axel Sauer, Katja Schwarz, and Andreas Geiger. 2022 · 2022
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Generative Adversarial Networks for Image Super-Resolution: A Survey
Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, and Yanning Zhang. 2022 · 2022
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Diffusion-GAN: Training GANs with Diffusion
Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022 · 2022
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CSCNet: Contextual semantic consistency network for trajectory prediction in crowded spaces
Beihao Xia, Conghao Wong, Qinmu Peng, Wei Yuan, and Xinge You. 2022 · 2022
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Stabilizing training of generative adversarial networks through regularization. In Advances in Neural Information Processing Systems . 2018–2028
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. 2017 · 2028
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