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This paper first presents a theory for generative adversarial methods that does not rely on the traditional minimax formulation.
Greedy function approximation: a gradient boosting machine
Friedman, J. H · 2001
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Statistical behavior and consistency of classification methods based on convex risk minimization
Zhang, T · 2004
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G · 2012
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
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Wasserstein generative adversarial networks
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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Mode regularized generative adversarial networks
Che, T., Li, Y., Jacob, A. P., Bengio, Y., and Li, W · 2017
MMD GAN: Towards deeper understanding of moment matching network
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Póczos, B · 2017
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Smolley, S. P · 2017
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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2017
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Fisher GAN
Mroueh, Y. and Sercu, T · 2017
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AdaGAN: Boosting generative models
Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C.-J., and Schölkopf, B · 2017
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Improving generative adversarial networks with denoising feature matching
Warde-Farley, D. and Bengio, Y · 2017
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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Introspective neural networks for generative modeling
Lazarow, J., Jin, L., and Tu, Z · 2017
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LR-GAN: Layered recursive generative adversarial networks for image generation
Yang, J., Kannan, A., Batra, D., and Parikh, D · 2017
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Gradient layer: Enhancing the convergence of adversarial training for generative models
Nitanda, A. and Suzuki, T · 2018
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