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Deep learning has achieved great success in the past few years.
Phase in speech and pictures
A Oppenheim, Jae Lim, Gary Kopec, and SC Pohlig · 1979
Earlier work this paper cites.
The fast fourier transform
Henri J Nussbaumer · 1981
Earlier work this paper cites.
The importance of phase in signals
Alan V Oppenheim and Jae S Lim · 1981
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Structural sparseness and spatial phase alignment in natural scenes
Bruce C Hansen and Robert F Hess · 2007
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L Reyes-Ortiz · 2012
Earlier work this paper cites.
Introducing a new benchmarked dataset for activity monitoring
Attila Reiss and Didier Stricker · 2012
Earlier work this paper cites.
Usc-had: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Mi Zhang and Alexander A Sawchuk · 2012
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
Earlier work this paper cites.
Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
Earlier work this paper cites.
Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units
Billur Barshan and Murat Cihan Yüksek · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Earlier work this paper cites.
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Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Cited alongside, same era.
Latent factors limiting the performance of semg-interfaces
Sergey Lobov, Nadia Krilova, Innokentiy Kastalskiy, Victor Kazantsev, and Valeri A Makarov · 2018
A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J Cook · 2020
Later among the works it cites.
Fda: Fourier domain adaptation for semantic segmentation
Yanchao Yang and Stefano Soatto · 2020
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Phase consistent ecological domain adaptation
Yanchao Yang, Dong Lao, Ganesh Sundaramoorthi, and Stefano Soatto · 2020
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Deep domain-adversarial image generation for domain generalisation
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang · 2020
Later among the works it cites.
Exploiting domain-specific features to enhance domain generalization
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Swad: Domain generalization by seeking flat minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park · 2021
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Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Support and invertibility in domain-invariant representations
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Cited alongside, same era.
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
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Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Hangwei Qian, Sinno Jialin Pan, Chunyan Miao, H Qian, SJ Pan, and C Miao · 2021
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Test-time fourier style calibration for domain generalization
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