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We introduce Exemplar VAEs, a family of generative models that bridge the gap between parametric and non-parametric, exemplar based generative models.
On estimation of a probability density function and mode
Emanuel Parzen · 1962
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Texture synthesis by non-parametric sampling
Alexei A Efros and Thomas K Leung · 1999
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira · 2001
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Example-based super-resolution
William T Freeman, Thouis R Jones, and Egon C Pasztor · 2002
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Object removal by exemplar-based inpainting
Antonio Criminisi, Patrick Perez, and Kentaro Toyama · 2003
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Distance metric learning with application to clustering with side-information
Eric P Xing, Michael I Jordan, Stuart J Russell, and Andrew Y Ng · 2003
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Normalized gradient with adaptive stepsize method for deep neural network training
Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, and Jaime Carbonell · 2004
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Scene completion using millions of photographs
James Hays and Alexei A Efros · 2007
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The manifold tangent classifier
Salah Rifai, Yann N Dauphin, Pascal Vincent, Yoshua Bengio, and Xavier Muller · 2011
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Scalable nearest neighbor algorithms for high dimensional data
Marius Muja and David G Lowe · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Block-normalized gradient method: An empirical study for training deep neural network
Adams Wei Yu, Lei Huang, Qihang Lin, Ruslan Salakhutdinov, and Jaime Carbonell · 2017
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Resampled priors for variational autoencoders
Matthias Bauer and Andriy Mnih · 2018
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Isolating sources of disentanglement in variational autoencoders
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Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
Towards conceptual compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, and Daan Wierstra · 2016
Cited alongside, same era.
Pixelvae: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2016
Cited alongside, same era.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Cited alongside, same era.
Elbo surgery: Yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Pixelsnail: An improved autoregressive generative model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2018
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Associative compression networks for representation learning
Alex Graves, Jacob Menick, and Aaron van den Oord · 2018
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Generating sentences by editing prototypes
Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang · 2018
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Vae with a vampprior
Jakub M Tomczak and Max Welling · 2018
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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Diagnosing and enhancing VAE models
Bin Dai and David Wipf · 2019
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From variational to deterministic autoencoders
Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf · 2019
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Faster autoaugment: Learning augmentation strategies using backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama · 2019
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2019
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Energy-inspired models: Learning with sampler-induced distributions
John Lawson, George Tucker, Bo Dai, and Rajesh Ranganath · 2019
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A forest from the trees: Generation through neighborhoods
Yang Li, Tianxiang Gao, and Junier Oliva · 2019
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Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
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Don’t blame the elbo! a linear vae perspective on posterior collapse
James Lucas, George Tucker, Roger B Grosse, and Mohammad Norouzi · 2019
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Classification accuracy score for conditional generative models
Suman Ravuri and Oriol Vinyals · 2019
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