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We present a novel method, SALAD, for the challenging vision task of adapting a pre-trained "source" domain network to a "target" domain, with a small budget for annotation in the "target" domain and a shift in the label space.
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Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
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Domain adaptation meets active learning
Piyush Rai, Avishek Saha, Hal Daumé III, and Suresh Venkatasubramanian · 2010
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Active supervised domain adaptation
Avishek Saha, Piyush Rai, Hal Daumé, Suresh Venkatasubramanian, and Scott L DuVall · 2011
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Scalable active learning for multiclass image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos P Papanikolopoulos · 2012
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Convolutional neural networks applied to house numbers digit classification
Pierre Sermanet, Soumith Chintala, and Yann LeCun · 2012
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
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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 separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 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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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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Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2017
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Autodial: Automatic domain alignment layers
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, and Samuel Rota Bulo · 2017
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Text-guided attention model for image captioning
Jonghwan Mun, Minsu Cho, and Bohyung Han · 2017
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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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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Learning to extract semantic structure from documents using multimodal fully convolutional neural networks
Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, and C Lee Giles · 2017
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Conditional generative adversarial network for structured domain adaptation
Weixiang Hong, Zhenzhen Wang, Ming Yang, and Junsong Yuan · 2018
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Cost-effective training of deep cnns with active model adaptation
Sheng-Jun Huang, Jia-Wei Zhao, and Zhao-Yang Liu · 2018
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Adaptive batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Xiaodi Hou, and Jiaying Liu · 2018
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Unpaired multi-domain image generation via regularized conditional gans
Xudong Mao and Qing Li · 2018
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Stochastic adversarial gradient embedding for active domain adaptation
Victor Bouvier, Philippe Very, Clément Chastagnol, Myriam Tami, and Céline Hudelot · 2020
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Domain adaptation without source data
Youngeun Kim, Donghyeon Cho, Kyeongtak Han, Priyadarshini Panda, and Sungeun Hong · 2020
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Divya Kothandaraman, Rohan Chandra, and Dinesh Manocha · 2020
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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
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Cross-domain document object detection: Benchmark suite and method
Kai Li, Curtis Wigington, Chris Tensmeyer, Handong Zhao, Nikolaos Barmpalios, Vlad I Morariu, Varun Manjunatha, Tong Sun, and Yun Fu · 2020
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Generate to adapt: Aligning domains using generative adversarial networks
Swami Sankaranarayanan, Yogesh Balaji, Carlos D Castillo, and Rama Chellappa · 2018
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 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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Cbam: Convolutional block attention module
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 2018
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
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Dual attention network for scene segmentation
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu · 2019
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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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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Active domain adaptation via clustering uncertainty-weighted embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman · 2020
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Active adversarial domain adaptation
Jong-Chyi Su, Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Subhransu Maji, and Manmohan Chandraker · 2020
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
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Alleviating semantic-level shift: A semi-supervised domain adaptation method for semantic segmentation
Zhonghao Wang, Yunchao Wei, Rogerio Feris, Jinjun Xiong, Wen-Mei Hwu, Thomas S Huang, and Honghui Shi · 2020
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Uncertainty reduction for model adaptation in semantic segmentation
Francois Fleuret et al · 2021
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Transferable query selection for active domain adaptation
Bo Fu, Zhangjie Cao, Jianmin Wang, and Mingsheng Long · 2021
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Masato Ishii and Masashi Sugiyama · 2021
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Domain adaptive knowledge distillation for driving scene semantic segmentation
Divya Kothandaraman, Athira M Nambiar, and Anurag Mittal · 2021
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Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, and Vinay P Namboodiri · 2021
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Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
Jian Liang, Dapeng Hu, Yunbo Wang, Ran He, and Jiashi Feng · 2021
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Source-free domain adaptation for semantic segmentation
Yuang Liu, Wei Zhang, and Jun Wang · 2021
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Viraj Prabhu, Shivam Khare, Deeksha Kartik, and Judy Hoffman · 2021
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Adaptive adversarial network for source-free domain adaptation
Haifeng Xia, Handong Zhao, and Zhengming Ding · 2021
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Discriminative active learning for domain adaptation
Fan Zhou, Changjian Shui, Shichun Yang, Bincheng Huang, Boyu Wang, and Brahim Chaib-draa · 2021
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Unsupervised robust domain adaptation without source data
Peshal Agarwal, Danda Pani Paudel, Jan-Nico Zaech, and Luc Van Gool · 2022
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Unsupervised batchnorm adaptation (ubna): A domain adaptation method for semantic segmentation without using source domain representations
Marvin Klingner, Jan-Aike Termöhlen, Jacob Ritterbach, and Tim Fingscheidt · 2022
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