2019

PixelVAE++: Improved PixelVAE with Discrete Prior

Sadeghi, Hossein, Andriyash, Evgeny, Vinci, Walter et al.

Understand

Constructing powerful generative models for natural images is a challenging task.

PixelCNN models capture details and local information in images very well but have limited receptive field.
Variational autoencoders with a factorial decoder can capture global information easily, but they often fail to reconstruct details faithfully.
PixelVAE combines the best features of the two models and constructs a generative model that is able to learn local and global structures.

Built on

Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Annealed importance sampling
Radford M Neal · 2001
Earlier work this paper cites.
Representational power of restricted boltzmann machines and deep belief networks
Nicolas Le Roux and Yoshua Bengio · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Inductive principles for restricted Boltzmann machine learning
Benjamin Marlin, Kevin Swersky, Bo Chen, and Nando Freitas · 2010
Earlier work this paper cites.
Efficient learning of domain-invariant image representations
Original
Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, and Kate Saenko · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Original
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Deep learning of representations: Looking forward
Yoshua Bengio · 2013
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Original
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Generative Adversarial Networks
Original
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Domain adaptive neural networks for object recognition
Muhammad Ghifary, W Bastiaan Kleijn, and Mengjie Zhang · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Original
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Original
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Variational recurrent auto-encoders
Original
Otto Fabius and Joost R van Amersfoort · 2014
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Original
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Techniques for learning binary stochastic feedforward neural networks
Original
Tapani Raiko, Mathias Berglund, Guillaume Alain, and Laurent Dinh · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Markov chain monte carlo and variational inference: Bridging the gap
Tim Salimans, Diederik P Kingma, Max Welling, et al · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Original
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Earlier work this paper cites.

Similar

Generating sentences from a continuous space
Original
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
Cited alongside, same era.
Importance weighted autoencoders
Original
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 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.
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.
Density estimation using real nvp
Original
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
Original
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Samplernn: An unconditional end-to-end neural audio generation model
Original
Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Cited alongside, same era.
One-shot learning with memory-augmented neural networks
Original
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Cited alongside, same era.

Then

Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
Later among the works it cites.
Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
Later among the works it cites.
Video pixel networks
Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2017
Later among the works it cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Later among the works it cites.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
Later among the works it cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Later among the works it cites.
PixelCNN++: Improving the pixelCNN with discretized logistic mixture likelihood and other modifications
Original
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Later among the works it cites.
Pixelsnail: An improved autoregressive generative model
Original
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2017
Later among the works it cites.
A hierarchical latent variable encoder-decoder model for generating dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio · 2017
Later among the works it cites.
Vae with a vampprior
Original
Jakub M Tomczak and Max Welling · 2017
Later among the works it cites.
Fixing a broken elbo
Original
Alexander A Alemi, Ben Poole, Ian Fischer, Joshua V Dillon, Rif A Saurous, and Kevin Murphy · 2017
Later among the works it cites.
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Geoffrey Roeder, Yuhuai Wu, and David K Duvenaud · 2017
Later among the works it cites.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
Dvae++: Discrete variational autoencoders with overlapping transformations
Original
Arash Vahdat, William G Macready, Zhengbing Bian, Amir Khoshaman, and Evgeny Andriyash · 2018
Later among the works it cites.
DVAE#: Discrete variational autoencoders with relaxed Boltzmann priors
Arash Vahdat, Evgeny Andriyash, and William G Macready · 2018
Later among the works it cites.
Gumbolt: Extending gumbel trick to boltzmann priors
Amir H Khoshaman and Mohammad Amin · 2018
Later among the works it cites.
Implicit generation and generalization in energy-based models
Original
Yilun Du and Igor Mordatch · 2019
Closest in time.
Learning undirected posteriors by backpropagation through mcmc updates
Original
Arash Vahdat, Evgeny Andriyash, and William G Macready · 2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…