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Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift.
The importance of shape in early lexical learning
Barbara Landau, Linda B Smith, and Susan S Jones · 1988
Earlier work this paper cites.
Transfer between picture books and the real world by very young children
Patricia A Ganea, Megan Bloom Pickard, and Judy S DeLoache · 2008
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Semi-supervised domain adaptation with instance constraints
Jeff Donahue, Judy Hoffman, Erik Rodner, Kate Saenko, and Trevor Darrell · 2013
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Domain adaptive neural networks for object recognition
Muhammad Ghifary, W Bastiaan Kleijn, and Mengjie Zhang · 2014
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Texture synthesis using convolutional neural networks
Leon Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 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
Earlier work this paper cites.
Semi-supervised domain adaptation with subspace learning for visual recognition
Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, and Tao Mei · 2015
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On the performance of googlenet and alexnet applied to sketches
Pedro Ballester and Ricardo Matsumura Araujo · 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, and Victor Lempitsky · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Scatter component analysis: A unified framework for domain adaptation and domain generalization
Muhammad Ghifary, David Balduzzi, W Bastiaan Kleijn, and Mengjie Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Deep convolutional networks do not classify based on global object shape
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip J Kellman · 2018
Adversarial dropout regularization
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Learning semantic representations for unsupervised domain adaptation
Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen · 2018
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Wieland Brendel and Matthias Bethge · 2019
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Domain generalization by solving jigsaw puzzles
Fabio Maria Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Wang, and Jia-Bin Huang · 2019
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Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
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Domain generalization with domain-specific aggregation modules
Antonio D’Innocente and Barbara Caputo · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Cycada: Cycle consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2018
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Assessing shape bias property of convolutional neural networks
Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, and Radha Poovendran · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
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Srm: A style-based recalibration module for convolutional neural networks
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Moment matching for multi-source domain adaptation
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Semi-supervised domain adaptation via minimax entropy
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Domain-symmetric networks for adversarial domain adaptation
Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan · 2019
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The origins and prevalence of texture bias in convolutional neural networks
Katherine Hermann, Ting Chen, and Simon Kornblith · 2020
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Domain generalization using a mixture of multiple latent domains
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