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In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds.
The MNIST database of handwritten digits
Y. LeCun · 1998
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook
N. Pinto, Z. Stone, T. Zickler, and D. Cox · 2011
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Evaluating open-universe face identification on the web
B. Becker and E. Ortiz · 2013
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Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Variational autoencoder based anomaly detection using reconstruction probability
J. An and S. Cho · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Autoencoding beyond pixels using a learned similarity metric
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
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J. Donahue, P. Krähenbühl, and T. Darrell · 2016
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Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
cleverhans v1.0.0: an adversarial machine learning library
N. Papernot, I. Goodfellow, R. Sheatsley, R. Feinman, and P. McDaniel · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
Cited alongside, same era.
Generative visual manipulation on the natural image manifold
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros · 2016
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Later among the works it cites.
Bigganex: A dive into the latent space of biggan, 2018
2018
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Universal decision-based black-box perturbations: Breaking security-through-obscurity defenses
T. A. Hogan and B. Kailkhura · 2018
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Ganomaly: Semi-supervised anomaly detection via adversarial training
S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon · 2018
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Solving bilinear inverse problems using deep generative priors
M. Asim, F. Shamshad, and A. Ahmed · 2018
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P. Bojanowski, A. Joulin, D. Lopez-Paz, and A. Szlam · 2017
Cited alongside, same era.
Compressed sensing using generative models
A. Bora, A. Jalal, E. Price, and A. G. Dimakis · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell · 2017
Cited alongside, same era.
The robust manifold defense: Adversarial training using generative models
A. Ilyas, A. Jalal, E. Asteri, C. Daskalakis, and A. G. Dimakis · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Cited alongside, same era.
Precise recovery of latent vectors from generative adversarial networks
Z. C. Lipton and S. Tripathi · 2017
Cited alongside, same era.
Later among the works it cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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Why do deep convolutional networks generalize so poorly to small image transformations?
A. Azulay and Y. Weiss · 2018
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Large scale gan training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2018
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Non-adversarial mapping with vaes
Y. Hoshen · 2018
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Nam: Non-adversarial unsupervised domain mapping
Y. Hoshen and L. Wolf · 2018
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
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Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2018
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Defending against adversarial attacks by leveraging an entire gan
G. K. Santhanam and P. Grnarova · 2018
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Solving linear inverse problems using GAN priors: An algorithm with provable guarantees
V. Shah and C. Hegde · 2018
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“zero-shot” super-resolution using deep internal learning
A. Shocher, N. Cohen, and M. Irani · 2018
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Efficient gan-based anomaly detection
H. Zenati, C. S. Foo, B. Lecouat, G. Manek, and V. R. Chandrasekhar · 2018
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Stylegan — encoder for official tensorflow implementation, 2019
2019
Closest in time.
Image2StyleGAN: How to embed images into the StyleGAN latent space?
R. Abdal, Y. Qin, and P. Wonka · 2019
Closest in time.