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Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs).
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., and Frasconi, P · 1994
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Efficient BackProp , pp. 9–50
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K. R · 1998
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The expected norm of random matrices
Seginer, Y · 2000
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
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Understanding the exploding gradient problem
Pascanu, R., Mikolov, T., and Bengio, Y · 2012
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe, A. M., McClelland, J. L., and Ganguli, S · 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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Xsede: Accelerating scientific discovery
Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., Hazlewood, V., Lathrop, S., Lifka, D., Peterson, G. D., Roskies, R., Scott, J. R., and Wilkins-Diehr, N · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Bridges: A uniquely flexible hpc resource for new communities and data analytics
Nystrom, N. A., Levine, M. J., Roskies, R. Z., and Scott, J. R · 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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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Cited alongside, same era.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Cited alongside, same era.
Neural photo editing with introspective adversarial networks
Brock, A., Lim, T., Ritchie, J. M., and Weston, N · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Generalizable adversarial training via spectral normalization
Farnia, F., Zhang, J. M., and Tse, D · 2018
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Regularisation of neural networks by enforcing lipschitz continuity
Gouk, H., Frank, E., Pfahringer, B., and Cree, M · 2018
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The relativistic discriminator: a key element missing from standard gan
Jolicoeur-Martineau, A · 2018
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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.
Improving generative adversarial networks with denoising feature matching
Warde-Farley, D. and Bengio, Y · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks, 2017
Arjovsky, M. and Bottou, L · 2017
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Bartlett, P. L., Foster, D. J., and Telgarsky, M. J · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 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
Cited alongside, same era.
Lee, A. X., Zhang, R., Ebert, F., Abbeel, P., Finn, C., and Levine, S · 2018
Later among the works it cites.
cgans with projection discriminator
Miyato, T. and Koyama, M · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Is generator conditioning causally related to gan performance?
Odena, A., Buckman, J., Olsson, C., Brown, T., Olah, C., Raffel, C., and Goodfellow, I · 2018
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How does batch normalization help optimization?
Santurkar, S., Tsipras, D., Ilyas, A., and Madry, A · 2018
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Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Tsuzuku, Y., Sato, I., and Sugiyama, M · 2018
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Improving the improved training of wasserstein gans: A consistency term and its dual effect
Wei, X., Gong, B., Liu, Z., Lu, W., and Wang, L · 2018
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A · 2018
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Free-form image inpainting with gated convolution
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T. S · 2019
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On the distance between two neural networks and the stability of learning
Bernstein, J., Vahdat, A., Yue, Y., and Liu, M.-Y · 2020
Closest in time.
Infogan-cr: Disentangling generative adversarial networks with contrastive regularizers
Lin, Z., Thekumparampil, K. K., Fanti, G., and Oh, S · 2020
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Precondition layer and its use for {gan}s, 2021
Fang, T., Schwing, A., and Sun, R · 2021
Closest in time.