Fetching the paper…
Reading the bibliography…
Deep neural networks have a clear degradation when applying to the unseen environment due to the covariate shift.
Approximate statistical tests for comparing supervised classification learning algorithms
Thomas G Dietterich · 1998
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet, Yoshua Bengio, et al · 2005
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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.
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.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Earlier work this paper cites.
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.
Domain adaptation in the absence of source domain data
Boris Chidlovskii, Stephane Clinchant, and Gabriela Csurka · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
Earlier work this paper cites.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 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.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Earlier work this paper cites.
Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
Cited alongside, same era.
Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
Cited alongside, same era.
The mapillary vistas dataset for semantic understanding of street scenes
Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder · 2017
Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Cited alongside, same era.
Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2018
Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification
Yixiao Ge, Dapeng Chen, and Hongsheng Li · 2020
Later among the works it cites.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
Later among the works it cites.
Augmix: A simple method to improve robustness and uncertainty under data shift
Dan Hendrycks*, Norman Mu*, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Later among the works it cites.
Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang · 2020
Later among the works it cites.
Minimum class confusion for versatile domain adaptation
Ying Jin, Ximei Wang, Mingsheng Long, and Jianmin Wang · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Cited alongside, same era.
Two at once: Enhancing learning and generalization capacities via ibn-net
Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang · 2018
Cited alongside, same era.
Learning to adapt structured output space for semantic segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
Cited alongside, same era.
Domain generalization by solving jigsaw puzzles
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
Cited alongside, same era.
Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu, and Heng Tao Shen · 2020
Later among the works it cites.
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
Later among the works it cites.
Domain generalization using a mixture of multiple latent domains
Toshihiko Matsuura and Tatsuya Harada · 2020
Later among the works it cites.
Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2020
Later among the works it cites.
A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
Later among the works it cites.
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
Later among the works it cites.
Adversarial domain adaptation with domain mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2020
Later among the works it cites.
Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
Later among the works it cites.
Domain generalization via entropy regularization
Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao · 2020
Later among the works it cites.
Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening
Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo · 2021
Closest in time.
Feature stylization and domain-aware contrastive learning for domain generalization
Seogkyu Jeon, Kibeom Hong, Pilhyeon Lee, Jewook Lee, and Hyeran Byun · 2021
Closest in time.
Source-free domain adaptation for semantic segmentation
Yuang Liu, Wei Zhang, and Jun Wang · 2021
Closest in time.
Generalization on unseen domains via inference-time label-preserving target projections
Prashant Pandey, Mrigank Raman, Sumanth Varambally, and Prathosh AP · 2021
Closest in time.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
Closest in time.
Logme: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long · 2021
Closest in time.
Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
Closest in time.