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We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
On contrastive divergence learning
Carreira-Perpinan, Miguel A and Hinton, Geoffrey · 2005
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A tutorial on energy-based learning
LeCun, Yann, Chopra, Sumit, and Hadsell, Raia · 2006
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Efficient learning of sparse representations with an energy-based model
Marc’Aurelio Ranzato, Christopher Poultney and Chopra, Sumit · 2007
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A unified energy-based framework for unsupervised learning
Ranzato, Marc’Aurelio, Boureau, Y-Lan, Chopra, Sumit, and LeCun, Yann · 2007
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Learning convolutional feature hierarchies for visual recognition
Kavukcuoglu, Koray, Sermanet, Pierre, Boureau, Y-Lan, Gregor, Karol, Mathieu, Michaël, and Cun, Yann L · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, Pascal, Larochelle, Hugo, Lajoie, Isabelle, Bengio, Yoshua, and Manzagol, Pierre-Antoine · 2010
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Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, Salah, Vincent, Pascal, Muller, Xavier, Glorot, Xavier, and Bengio, Yoshua · 2011
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, Emily L, Chintala, Soumith, Fergus, Rob, et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Deep learning face attributes in the wild
Liu, Ziwei, Luo, Ping, Wang, Xiaogang, and Tang, Xiaoou · 2015
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Deep multi-scale video prediction beyond mean square error
Mathieu, Michael, Couprie, Camille, and LeCun, Yann · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, Fisher, Seff, Ari, Zhang, Yinda, Song, Shuran, Funkhouser, Thomas, and Xiao, Jianxiong · 2015
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Stacked what-where auto-encoders
Zhao, Junbo, Mathieu, Michael, Goroshin, Ross, and Lecun, Yann · 2015
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Generating images with recurrent adversarial networks
Im, Daniel Jiwoong, Kim, Chris Dongjoo, Jiang, Hui, and Memisevic, Roland · 2016
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Deep directed generative models with energy-based probability estimation
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Deconstructing the ladder network architecture
Pezeshki, Mohammad, Fan, Linxi, Brakel, Philemon, Courville, Aaron, and Bengio, Yoshua · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2015
Cited alongside, same era.
Semi-supervised learning with ladder networks
Rasmus, Antti, Berglund, Mathias, Honkala, Mikko, Valpola, Harri, and Raiko, Tapani · 2015
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Kim, Taesup and Bengio, Yoshua · 2016
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Improved techniques for training gans
Salimans, Tim, Goodfellow, Ian, Zaremba, Wojciech, Cheung, Vicki, Radford, Alec, and Chen, Xi · 2016
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Matching networks for one shot learning
Vinyals, Oriol, Blundell, Charles, Lillicrap, Timothy, Kavukcuoglu, Koray, and Wierstra, Daan · 2016
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