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We propose Unbalanced GANs, which pre-trains the generator of the generative adversarial network (GAN) using variational autoencoder (VAE).
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A. L., Hannun, A. Y., and Ng, A. Y · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., and Winther, O · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Energy-based generative adversarial network
Zhao, J., Mathieu, M., and LeCun, Y · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Began: Boundary equilibrium generative adversarial networks
Berthelot, D., Schumm, T., and Metz, L · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S · 2017
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Transferring gans: generating images from limited data
Wang, Y., Wu, C., Herranz, L., van de Weijer, J., Gonzalez-Garcia, A., and Raducanu, B · 2018
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