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Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE).
Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Curriculum learning
Yoshua Bengio, Jerome Louradour, Ronan Collobert, and Jason Weston · 2015
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus · 2015
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra · 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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An architecture for deep, hierarchical generative models
Philip Bachman · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Learning to generate with memory
Chongxuan Li, Jun Zhu, and Bo Zhang · 2016
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Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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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 · 2017
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dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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A fully progressive approach to single-image super-resolution
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, and Christopher Schroers · 2018
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Stackgan++: Realistic image synthesis with stacked generative adversarial networks
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Learning hierarchical features from deep generative models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
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Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2018
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Improving disentangled representation learning with the beta bernoulli process
Prashnna Kumar Gyawali, Zhiyuan Li, Cameron Knight, Sandesh Ghimire, B Milan Horacek, John Sapp, and Linwei Wang · 2019
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Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision
José Lezama · 2019
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