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We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
Animating rotation with quaternion curves
K. Shoemake · 1985
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H. Drucker and Y. L. Cun · 1992
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Learning factorial codes by predictability minimization
J. Schmidhuber · 1992
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Disentangling factors of variation via generative entangling
G. Desjardins, A. Courville, and Y. Bengio · 2012
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Ng · 2013
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Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, and T. Brox · 2014
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Generative Adversarial Networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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One millisecond face alignment with an ensemble of regression trees
V. Kazemi and J. Sullivan · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Deep generative image models using a Laplacian pyramid of adversarial networks
E. L. Denton, S. Chintala, A. Szlam, and R. Fergus · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
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TensorFlow: a system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
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InfoGAN: interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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J. Donahue, P. Krähenbühl, and T. Darrell · 2016
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A learned representation for artistic style
V. Dumoulin, J. Shlens, and M. Kudlur · 2016
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Generative multi-adversarial networks
I. P. Durugkar, I. Gemp, and S. Mahadevan · 2016
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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A survey of inductive biases for factorial representation-learning
K. Ridgeway · 2016
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Improved techniques for training GANs
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Sampling generative networks: Notes on a few effective techniques
T. White · 2016
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On the emergence of invariance and disentangling in deep representations
A. Achille and S. Soatto · 2017
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Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville · 2017
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Online adaptative curriculum learning for GANs
T. Doan, J. Monteiro, I. Albuquerque, B. Mazoure, A. Durand, J. Pineau, and R. D. Hjelm · 2018
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Feature-wise transformations
V. Dumoulin, E. Perez, N. Schucher, F. Strub, H. d. Vries, A. Courville, and Y. Bengio · 2018
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A framework for the quantitative evaluation of disentangled representations
C. Eastwood and C. K. I. Williams · 2018
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MIXGAN: learning concepts from different domains for mixture generation
W.-S. Z. Guang-Yuan Hao, Hong-Xing Yu · 2018
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Multimodal unsupervised image-to-image translation
X. Huang, M. Liu, S. J. Belongie, and J. Kautz · 2018
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G. Ghiasi, H. Lee, M. Kudlur, V. Dumoulin, and J. Shlens · 2017
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Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. J. Belongie · 2017
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Progressive growing of GANs for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
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Universal style transfer via feature transforms
Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu, and M.-H. Yang · 2017
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Disentangling by factorising
H. Kim and A. Mnih · 2018
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Glow: Generative flow with invertible 1x1 convolutions
D. P. Kingma and P. Dhariwal · 2018
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The gan landscape: Losses, architectures, regularization, and normalization
K. Kurach, M. Lucic, X. Zhai, M. Michalski, and S. Gelly · 2018
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Feature-based metrics for exploring the latent space of generative models
S. Laine · 2018
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Which training methods for GANs do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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cGANs with projection discriminator
T. Miyato and M. Koyama · 2018
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Dropout-gan: Learning from a dynamic ensemble of discriminators
G. Mordido, H. Yang, and C. Meinel · 2018
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ClusterGAN : Latent space clustering in generative adversarial networks
S. Mukherjee, H. Asnani, E. Lin, and S. Kannan · 2018
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T. Sainburg, M. Thielk, B. Theilman, B. Migliori, and T. Gentner · 2018
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Improved training with curriculum gans
R. Sharma, S. Barratt, S. Ermon, and V. Pande · 2018
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Whitening and coloring transform for GANs
A. Siarohin, E. Sangineto, and N. Sebe · 2018
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Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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GAN dissection: Visualizing and understanding generative adversarial networks
D. Bau, J. Zhu, H. Strobelt, B. Zhou, J. B. Tenenbaum, W. T. Freeman, and A. Torralba · 2019
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Making convolutional networks shift-invariant again, 2019
R. Zhang · 2019
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