Fetching the paper…
Reading the bibliography…
Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-fitting.
Edgeconnect: Generative image inpainting with adversarial edge learning
Nazeri, K.; Ng, E.; Joseph, T.; Qureshi, F. Z.; and Ebrahimi, M. 2019 · 1901
Earlier work this paper cites.
Consistency regularization for generative adversarial networks
Zhang, H.; Zhang, Z.; Odena, A.; and Lee, H. 2019b · 1910
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Blum, A.; and Mitchell, T. 1998 · 1998
Earlier work this paper cites.
Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J.; Tzeng, E.; Park, T.; Zhu, J.-Y.; Isola, P.; Saenko, K.; Efros, A.; and Darrell, T. 2018 · 1998
Earlier work this paper cites.
Towards GAN benchmarks which require generalization
Gulrajani, I.; Raffel, C.; and Metz, L. 2020 · 2001
Earlier work this paper cites.
Freeze Discriminator: A Simple Baseline for Fine-tuning GANs
Mo, S.; Cho, M.; and Shin, J. 2020 · 2002
Earlier work this paper cites.
Training generative adversarial networks with limited data
Karras, T.; Aittala, M.; Hellsten, J.; Laine, S.; Lehtinen, J.; and Aila, T. 2020a · 2006
Earlier work this paper cites.
Differentiable augmentation for data-efficient gan training
Zhao, S.; Liu, Z.; Lin, J.; Zhu, J.-Y.; and Han, S. 2020 · 2006
Earlier work this paper cites.
Learning hybrid image templates (HIT) by information projection
Si, Z.; and Zhu, S.-C. 2011 · 2011
Earlier work this paper cites.
Robust co-training
Sun, S.; and Jin, F. 2011 · 2011
Earlier work this paper cites.
Bayesian co-training
Yu, S.; Krishnapuram, B.; Rosales, R.; and Rao, R. B. 2011 · 2011
Earlier work this paper cites.
Understanding the exploding gradient problem
Pascanu, R.; Mikolov, T.; and Bengio, Y. 2012 · 2012
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Pascanu, R.; Mikolov, T.; and Bengio, Y. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F.; Seff, A.; Zhang, Y.; Song, S.; Funkhouser, T.; and Xiao, J. 2015 · 2015
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Earlier work this paper cites.
Amortised map inference for image super-resolution
Sønderby, C. K.; Caballero, J.; Theis, L.; Shi, W.; and Huszár, F. 2016 · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M.; Chintala, S.; and Bottou, L. 2017 · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; and Courville, A. 2017 · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017 · 2017
Cited alongside, same era.
Deep co-training for semi-supervised image recognition
Qiao, S.; Shen, W.; Zhang, Z.; Wang, B.; and Yuille, A. 2018 · 2018
Later among the works it cites.
Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K.; Watanabe, K.; Ushiku, Y.; and Harada, T. 2018 · 2018
Later among the works it cites.
Generative image inpainting with contextual attention
Yu, J.; Lin, Z.; Yang, J.; Shen, X.; Lu, X.; and Huang, T. S. 2018 · 2018
Later among the works it cites.
Seeing what a gan cannot generate
Bau, D.; Zhu, J.-Y.; Wulff, J.; Peebles, W.; Strobelt, H.; Zhou, B.; and Torralba, A. 2019 · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Later among the works it cites.
Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2017 · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C.; Theis, L.; Huszár, F.; Caballero, J.; Cunningham, A.; Acosta, A.; Aitken, A.; Tejani, A.; Totz, J.; Wang, Z.; et al. 2017 · 2017
Cited alongside, same era.
Least squares generative adversarial networks
Mao, X.; Li, Q.; Xie, H.; Lau, R. Y.; Wang, Z.; and Paul Smolley, S. 2017 · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; and Batra, D. 2017 · 2017
Cited alongside, same era.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H.; Xu, T.; Li, H.; Zhang, S.; Wang, X.; Huang, X.; and Metaxas, D. N. 2017 · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y.; Park, T.; Isola, P.; and Efros, A. A. 2017 · 2017
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2018 · 2018
Cited alongside, same era.
Luo, Y.; Zheng, L.; Guan, T.; Yu, J.; and Yang, Y. 2019 · 2019
Later among the works it cites.
Image generation from small datasets via batch statistics adaptation
Noguchi, A.; and Harada, T. 2019 · 2019
Later among the works it cites.
Semantic image synthesis with spatially-adaptive normalization
Park, T.; Liu, M.-Y.; Wang, T.-C.; and Zhu, J.-Y. 2019 · 2019
Later among the works it cites.
Detecting overfitting of deep generative networks via latent recovery
Webster, R.; Rabin, J.; Simon, L.; and Jurie, F. 2019 · 2019
Later among the works it cites.
Free-form image inpainting with gated convolution
Yu, J.; Lin, Z.; Yang, J.; Shen, X.; Lu, X.; and Huang, T. S. 2019 · 2019
Later among the works it cites.
Progressive domain adaptation for object detection
Hsu, H.-K.; Yao, C.-H.; Tsai, Y.-H.; Hung, W.-C.; Tseng, H.-Y.; Singh, M.; and Yang, M.-H. 2020 · 2020
Later among the works it cites.
Diverse image generation via self-conditioned gans
Liu, S.; Wang, T.; Bau, D.; Zhu, J.-Y.; and Torralba, A. 2020 · 2020
Later among the works it cites.
Minegan: effective knowledge transfer from gans to target domains with few images
Wang, Y.; Gonzalez-Garcia, A.; Berga, D.; Herranz, L.; Khan, F. S.; and Weijer, J. v. d. 2020 · 2020
Later among the works it cites.
Regularizing Generative Adversarial Networks under Limited Data
Tseng, H.-Y.; Jiang, L.; Liu, C.; Yang, M.-H.; and Yang, W. 2021 · 2021
Closest in time.
Defect-GAN: High-fidelity defect synthesis for automated defect inspection
Zhang, G.; Cui, K.; Hung, T.-Y.; and Lu, S. 2021 · 2021
Closest in time.