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Generative Adversarial Networks (GANs) have significantly advanced image synthesis, however, the synthesis quality drops significantly given a limited amount of training data.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2003
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Image augmentations for gan training
Z. Zhao, Z. Zhang, T. Chen, S. Singh, and H. Zhang · 2006
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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One millisecond face alignment with an ensemble of regression trees
V. Kazemi and J. Sullivan · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Learning features by watching objects move
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
Cited alongside, same era.
Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2019
Later among the works it cites.
Stargan v2: Diverse image synthesis for multiple domains
Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Data-efficient image recognition with contrastive predictive coding
O. Henaff · 2020
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Contragan: Contrastive learning for conditional image generation
M. Kang and J. Park · 2020
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
Cited alongside, same era.
Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
Cited alongside, same era.
Self-supervised gans via auxiliary rotation loss
T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby · 2019
Cited alongside, same era.
Large scale adversarial representation learning
J. Donahue and K. Simonyan · 2019
Cited alongside, same era.
Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
Later among the works it cites.
Contrastive learning for unpaired image-to-image translation
T. Park, A. A. Efros, R. Zhang, and J.-Y. Zhu · 2020
Later among the works it cites.
Consistency regularization for generative adversarial networks
H. Zhang, Z. Zhang, A. Odena, and H. Lee · 2020
Later among the works it cites.
Training gans with stronger augmentations via contrastive discriminator
J. Jeong and J. Shin · 2021
Closest in time.
Divco: Diverse conditional image synthesis via contrastive generative adversarial network
R. Liu, Y. Ge, C. L. Choi, X. Wang, and H. Li · 2021
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On data augmentation for gan training
N.-T. Tran, V.-H. Tran, N.-B. Nguyen, T.-K. Nguyen, and N.-M. Cheung · 2021
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Generative hierarchical features from synthesizing images
Y. Xu, Y. Shen, J. Zhu, C. Yang, and B. Zhou · 2021
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Instance localization for self-supervised detection pretraining
C. Yang, Z. Wu, B. Zhou, and S. Lin · 2021
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Dual contrastive loss and attention for gans
N. Yu, G. Liu, A. Dundar, A. Tao, B. Catanzaro, L. Davis, and M. Fritz · 2021
Closest in time.