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Multi-source Domain Adaptation (MDA) aims to transfer predictive models from multiple, fully-labeled source domains to an unlabeled target domain.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning from multiple sources
Koby Crammer, Michael Kearns, and Jennifer Wortman · 2008
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2008
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Domain adaptation from multiple sources via auxiliary classifiers
Lixin Duan, Ivor W Tsang, Dong Xu, and Tat-Seng Chua · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Domain adaptation for object recognition: An unsupervised approach
Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2011
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A two-stage weighting framework for multi-source domain adaptation
Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, and Jieping Ye · 2011
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Multiple source adaptation and the rényi divergence
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Domain adaptation from multiple sources: A domain-dependent regularization approach
Lixin Duan, Dong Xu, and Ivor Wai-Hung Tsang · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
Lixin Duan, Dong Xu, and Shih-Fu Chang · 2012
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 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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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Domain adaptive neural networks for object recognition
Muhammad Ghifary, W Bastiaan Kleijn, and Mengjie Zhang · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Domain generalization for object recognition with multi-task autoencoders
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 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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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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A survey of multi-source domain adaptation
Shiliang Sun, Honglei Shi, and Yuanbin Wu · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 2016
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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Cited alongside, same era.
Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Cited alongside, same era.
Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2017
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
Multi-source domain adaptation for semantic segmentation
Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, and Kurt Keutzer · 2019
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Domain generalization by solving jigsaw puzzles
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
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Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
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Self-supervised adaptation of high-fidelity face models for monocular performance tracking
Jae Shin Yoon, Takaaki Shiratori, Shoou-I Yu, and Hyun Soo Park · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Cited alongside, same era.
Deep unsupervised convolutional domain adaptation
Junbao Zhuo, Shuhui Wang, Weigang Zhang, and Qingming Huang · 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.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
Cited alongside, same era.
Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
Cited alongside, same era.
Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
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Multi-source distilling domain adaptation
Sicheng Zhao, Guangzhi Wang, Shanghang Zhang, Yang Gu, Yaxian Li, Zhichao Song, Pengfei Xu, Runbo Hu, Hua Chai, and Kurt Keutzer · 2020
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The covid-19 epidemic
Thirumalaisamy P Velavan and Christian G Meyer · 2020
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Covid-19: immunopathology and its implications for therapy
Xuetao Cao · 2020
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Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets
Stephanie A Harmon, Thomas H Sanford, Sheng Xu, Evrim B Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, et al · 2020
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Cross-domain self-supervised learning for domain adaptation with few source labels
Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh, Bryan A Plummer, Stan Sclaroff, and Kate Saenko · 2020
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Learning to combine: Knowledge aggregation for multi-source domain adaptation
Hang Wang, Minghao Xu, Bingbing Ni, and Wenjun Zhang · 2020
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Implicit class-conditioned domain alignment for unsupervised domain adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin, and Mohammad Havaei · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2020
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Pre-training by completing point clouds
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matthew J Kusner · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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Self-labelling via simultaneous clustering and representation learning
Yuki M. Asano, Christian Rupprecht, and Andrea Vedaldi · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Self-supervised learning for domain adaptation on point-clouds
Idan Achituve, Haggai Maron, and Gal Chechik · 2020
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Self-supervised domain adaptation for patient-specific, real-time tissue tracking
Sontje Ihler, Felix Kuhnke, Max-Heinrich Laves, and Tobias Ortmaier · 2020
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Action segmentation with joint self-supervised temporal domain adaptation
Min-Hung Chen, Baopu Li, Yingze Bao, Ghassan AlRegib, and Zsolt Kira · 2020
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Self-supervised sim-to-real adaptation for visual robotic manipulation
Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe, Konstantinos Bousmalis, and Francesco Nori · 2020
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Unsupervised multi-source domain adaptation without access to source data
Sk Miraj Ahmed, Dripta S Raychaudhuri, Sujoy Paul, Samet Oymak, and Amit K Roy-Chowdhury · 2021
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Your classifier can secretly suffice multi-source domain adaptation
Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, and R Venkatesh Babu · 2021
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Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation
Xiangyu Yue, Zangwei Zheng, Shanghang Zhang, Yang Gao, Trevor Darrell, Kurt Keutzer, and Alberto Sangiovanni Vincentelli · 2021
Closest in time.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven C.H. Hoi · 2021
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Prototype-based multisource domain adaptation
Lihua Zhou, Mao Ye, Dan Zhang, Ce Zhu, and Luping Ji · 2021
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Self-supervised pretraining improves self-supervised pretraining
Colorado J Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, et al · 2021
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Region similarity representation learning
Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, and Trevor Darrell · 2021
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