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In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H. (1989) · 1989
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Introduction to Reinforcement Learning
Sutton, R. S. and Barto, A. G. (1998) · 1998
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Numerical Optimization
Nocedal, J. and Wright, S. (2006) · 2006
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Optimal Transport: Old and New
Villani, C. (2008) · 2008
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009) · 2009
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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) · 2014
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Zaremba, W. and Sutskever, I. (2014) · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
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A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M. (2015) · 2015
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Improved generator objectives for gans
Poole, B., Alemi, A. A., Sohl-Dickstein, J., and Angelova, A. (2016) · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
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Improving generative adversarial networks with denoising feature matching
Warde-Farley, D. and Bengio, Y. (2016) · 2016
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017) · 2017
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O. (2017) · 2017
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Language generation with recurrent generative adversarial networks without pre-training
Press, O., Bar, A., Bogin, B., Berant, J., and Wolf, L. (2017) · 2017
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Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)
Sanchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., and Aspuru-Guzik, A. (2017) · 2017
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Stackgan++: Realistic image synthesis with stacked generative adversarial networks
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Seqgan: Sequence generative adversarial nets with policy gradient
Yu, L., Zhang, W., Wang, J., and Yu, Y. (2016) · 2016
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Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
Esteban, C., Hyland, S. L., and Rätsch, G. (2017) · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017) · 2017
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Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. N. (2017) · 2017
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Barratt, S. and Sharma, R. (2018) · 2018
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The malicious use of artificial intelligence: Forecasting, prevention, and mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., Anderson, H. S., Roff, H., Allen, G. C., Steinhardt, J., Flynn, C., hÉigeartaigh, S. Ó., Beard, S., Belfield, H., Farquhar, S., Lyle, C., Crootof, R., Evans, O., Page, M., Bryson, J., Yampolskiy, R., and Amodei, D. (2018) · 2018
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Maskgan: Better text generation via filling in the______
Fedus, W., Goodfellow, I., and Dai, A. M. (2018) · 2018
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