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Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
Representation learning a review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Learning to disentangle factors of variation with manifold interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 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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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
DARLA: improving zero-shot transfer in reinforcement learning
Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
Earlier work this paper cites.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Earlier work this paper cites.
dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Cited alongside, same era.
Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin · 2018
Cited alongside, same era.
3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B. Grosse, and David Duvenaud · 2018
Cited alongside, same era.
Learning disentangled joint continuous and discrete representations
Emilien Dupont · 2018
Cited alongside, same era.
Hyunjik Kim and Andriy Mnih · 2018
From variational to deterministic autoencoders
Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf · 2019
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Group-based learning of disentangled representations with generalizability for novel contents
Haruo Hosoya · 2019
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Disentangling geometry and appearance with regularised geometry-aware generative adversarial networks
Linh Tran, Jean Kossaifi, Yannis Panagakis, and Maja Pantic · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Cited alongside, same era.
Learning latent subspaces in variational autoencoders
Jack Klys, Jake Snell, and Richard S. Zemel · 2018
Cited alongside, same era.
GAGAN: geometry-aware generative adversarial networks
Jean Kossaifi, Linh Tran, Yannis Panagakis, and Maja Pantic · 2018
Cited alongside, same era.
Deep representation-decoupling neural networks for monaural music mixture separation
Zhuo Li, Hongwei Wang, Miao Zhao, Wenjie Li, and Minyi Guo · 2018
Cited alongside, same era.
Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
Cited alongside, same era.
Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
Cited alongside, same era.
On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem
Cited in the paper.
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
Later among the works it cites.
Weakly supervised disentanglement with guarantees
Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Later among the works it cites.
Is independence all you need? on the generalization of representations learned from correlated data
Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, and Stefan Bauer · 2020
Later among the works it cites.
Fine-grained 3d shape classification with hierarchical part-view attentions
Xinhai Liu, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker · 2021
Closest in time.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Consistency regularization for variational auto-encoders
Samarth Sinha and Adji B Dieng · 2021
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