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We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain.
On Learning Invariant Representation for Domain Adaptation
Han Zhao, Remi Tachet des Combes, Kun Zhang, and Geoffrey J. Gordon · 1901
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BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Lars Maaløe, Marco Fraccaro, Valentin Liévin, and Ole Winther · 1902
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Diagnosing and Enhancing VAE Models
Bin Dai and David Wipf · 1903
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Learning Robust Representations by Projecting Superficial Statistics Out
Haohan Wang, Zexue He, Zachary C. Lipton, and Eric P. Xing · 1903
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Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, and Dmitry Vetrov · 1905
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Deep Domain Generalization With Structured Low-Rank Constraint
Zhengming Ding and Yun Fu · 1941
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Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2013
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Domain Generalization via Invariant Feature Representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, and Max Welling · 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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Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2015
Cited alongside, same era.
Domain Generalization for Object Recognition with Multi-task Autoencoders
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 2015
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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.
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin · 2018
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Understanding disentangling in beta-VAE
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Agnostic Domain Generalization
Fabio M. Carlucci, Paolo Russo, Tatiana Tommasi, and Barbara Caputo · 2018
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Towards a Definition of Disentangled Representations
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
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Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
Cited alongside, same era.
Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris Nagtegaal, and Jeroen van der Laak · 2017
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 · 2017
Cited alongside, same era.
Domain-adversarial neural networks to address the appearance variability of histopathology images
Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, and Mitko Veta · 2017
Cited alongside, same era.
Learning to Generalize: Meta-Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
Cited alongside, same era.
Unified Deep Supervised Domain Adaptation and Generalization
Saeid Motiian, Marco Piccirilli, Donald A. Adjeroh, and Gianfranco Doretto · 2017
Cited alongside, same era.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma · 2017
Cited alongside, same era.
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, and Philip H. S. Torr · 2017
Cited alongside, same era.
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2018
Later among the works it cites.
Learning Latent Subspaces in Variational Autoencoders
Jack Klys, Jake Snell, and Richard Zemel · 2018
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Domain Generalization via Conditional Invariant Representation
Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao · 2018
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2018
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Best sources forward: domain generalization through source-specific nets
Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, and Elisa Ricci · 2018
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Invariant Representations without Adversarial Training
Daniel Moyer, Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, and Aram Galstyan · 2018
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Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Sivaramakrishnan Rajaraman, Sameer K. Antani, Mahdieh Poostchi, Kamolrat Silamut, Md. A. Hossain, Richard J. Maude, Stefan Jaeger, and George R. Thoma · 2018
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Generalizing Across Domains via Cross-Gradient Training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi · 2018
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