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In many practical applications, it is often difficult and expensive to obtain enough large-scale labeled data to train deep neural networks to their full capability.
A database for handwritten text recognition research
Jonathan J. Hull · 1994
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2008
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, and Afshin. Rostamizadeh · 2009
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An empirical analysis of domain adaptation algorithms for genomic sequence analysis
Gabriele Schweikert, Gunnar Rätsch, Christian Widmer, and Bernhard Schölkopf · 2009
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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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Affective image classification using features inspired by psychology and art theory
Jana Machajdik and Allan Hanbury · 2010
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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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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 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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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Marginalized denoising autoencoders for domain adaptation
Minmin Chen, Zhixiang Xu, Kilian Q Weinberger, and Fei Sha · 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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Overview of the 2012 shared task on parsing the web
Slav Petrov and Ryan McDonald · 2012
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Large-scale visual sentiment ontology and detectors using adjective noun pairs
Damian Borth, Rongrong Ji, Tao Chen, Thomas Breuel, and Shih-Fu Chang · 2013
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Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
Boqing Gong, Kristen Grauman, and Fei Sha · 2013
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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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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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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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A survey of multi-source domain adaptation
Shiliang Sun, Honglei Shi, and Yuanbin Wu · 2015
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Robust image sentiment analysis using progressively trained and domain transferred deep networks
Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang · 2015
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Multi-source iterative adaptation for cross-domain classification
Himanshu S Bhatt, Arun Rajkumar, and Shourya Roy · 2016
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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
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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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Deep reconstruction-classification networks for unsupervised domain adaptation
Boosting domain adaptation by discovering latent domains
Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, and Elisa Ricci · 2018
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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
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Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo, Junjie Yan, and Liang Lin · 2018
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Bdd100k: A diverse driving video database with scalable annotation tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell · 2018
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Adversarial multiple source domain adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J Gordon · 2018
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Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
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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
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Building a large scale dataset for image emotion recognition: The fine print and the benchmark
Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang · 2016
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Fine-grained recognition in the wild: A multi-task domain adaptation approach
Timnit Gebru, Judy Hoffman, and Li Fei-Fei · 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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Adversarial multi-task learning for text classification
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang · 2017
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Emotiongan: Unsupervised domain adaptation for learning discrete probability distributions of image emotions
Sicheng Zhao, Xin Zhao, Guiguang Ding, and Kurt Keutzer · 2018
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Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Unsupervised multi-source domain adaptation driven by deep adversarial ensemble learning
Sayan Rakshit, Biplab Banerjee, Gemma Roig, and Subhasis Chaudhuri · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2019
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Towards multi-source adaptive semantic segmentation
Paolo Russo, Tatiana Tommasi, and Barbara Caputo · 2019
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Tmda: Task-specific multi-source domain adaptation via clustering embedded adversarial training
Haotian Wang, Wenjing Yang, Zhipeng Lin, and Yue Yu · 2019
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Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud
Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer · 2019
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Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong · 2019
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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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Cycleemotiongan: Emotional semantic consistency preserved cyclegan for adapting image emotions
Sicheng Zhao, Chuang Lin, Pengfei Xu, Sendong Zhao, Yuchen Guo, Ravi Krishna, Guiguang Ding, and Kurt Keutzer · 2019
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Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources
Yongchun Zhu, Fuzhen Zhuang, and Deqing Wang · 2019
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Multi-source domain adaptation for text classification via distancenet-bandits
Han Guo, Ramakanth Pasunuru, and Mohit Bansal · 2020
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Multi-source domain adaptation for visual sentiment classification
Chuang Lin, Sicheng Zhao, Lei Meng, and Tat-Seng Chua · 2020
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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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