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Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics.
Domain adaptation for statistical classifiers
Hal Daume III and Daniel Marcu · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe · 2008
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3d laser scan classification using web data and domain adaptation
Kevin Lai and Dieter Fox · 2009
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Study of the photodetector characteristics of a camera for color constancy in natural scenes
Sivalogeswaran Ratnasingam and Steve Collins · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Seqslam: Visual route-based navigation for sunny summer days and stormy winter nights
Michael J Milford and Gordon F Wyeth · 2012
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Experience-based Navigation for Long-term Localisation
Winston Churchill and Paul Newman · 2013
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Dealing with shadows: Capturing intrinsic scene appearance for image-based outdoor localisation
Peter Corke, Rohan Paul, Winston Churchill, and Paul Newman · 2013
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Appearance change prediction for long-term navigation across seasons
Peer Neubert, Niko Sunderhauf, and Peter Protzel · 2013
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Illumination invariant imaging: Applications in robust vision-based localisation, mapping and classification for autonomous vehicles
Will Maddern, Alex Stewart, Colin McManus, Ben Upcroft, Winston Churchill, and Paul Newman · 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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Domain-adversarial neural networks
Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, and Mario Marchand · 2014
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It’s not easy seeing green: Lighting-resistant stereo visual teach & repeat using color-constant images
Michael Paton, Kirk MacTavish, Chris J Ostafew, and Timothy D Barfoot · 2015
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Towards adapting deep visuomotor representations from simulated to real environments
Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Xingchao Peng, Sergey Levine, Kate Saenko, and Trevor Darrell · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Jianmin Wang, and Michael I. Jordan · 2016
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Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian F. Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, and Ben Glocker · 2016
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Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy
Dan Barnes, William P. Maddern, and Ingmar Posner · 2016
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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2016
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
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Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky, Urun Dogan, Marius Kloft, Francesco Orabona, and Tatiana Tommasi · 2016
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2016
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Will Maddern, Geoff Pascoe, Chris Linegar, and Paul Newman · 2016
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What makes imagenet good for transfer learning?
Mi-Young Huh, Pulkit Agrawal, and Alexei A. Efros · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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NIPS 2016 tutorial: Generative adversarial networks
Ian J. Goodfellow · 2017
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Sspp-dan: Deep domain adaptation network for face recognition with single sample per person
Sungeun Hong, Woobin Im, Jongbin Ryu, and Hyun S Yang · 2017
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