Fetching the paper…
Reading the bibliography…
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate.This setting neglects the more practical scenario where training data are collected from multiple sources.
A constructive definition of dirichlet priors
Jayaram Sethuraman · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
Earlier work this paper cites.
Domain adaptation meets active learning
Piyush Rai, Avishek Saha, Hal Daumé III, and Suresh Venkatasubramanian · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
A two-stage weighting framework for multi-source domain adaptation
Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, and Jieping Ye · 2011
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
A survey of multi-source domain adaptation
Shiliang Sun, Honglei Shi, and Yuanbin Wu · 2015
Earlier work this paper cites.
David Ha, Andrew Dai, and Quoc V Le · 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.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le · 2017
Earlier work this paper cites.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Earlier work this paper cites.
SMASH: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston · 2018
Earlier work this paper cites.
Domain adaptive faster r-cnn for object detection in the wild
Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
Earlier work this paper cites.
Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 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.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
Cited alongside, same era.
Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo, Junjie Yan, and Liang Lin · 2018
Cited alongside, same era.
Adversarial multiple source domain adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J Gordon · 2018
Your classifier can secretly suffice multi-source domain adaptation
Naveen Venkat, Jogendra Nath Kundu, Durgesh Singh, Ambareesh Revanur, et al · 2020
Later among the works it cites.
Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, and Benjamin F. Grewe · 2020
Later among the works it cites.
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
Later among the works it cites.
Evidential deep learning for open set action recognition
Wentao Bao, Qi Yu, and Yu Kong · 2021
Later among the works it cites.
Discrepancy-based active learning for domain adaptation
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, and Nicolas Vayatis · 2021
Later among the works it cites.
Transferable query selection for active domain adaptation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
Cited alongside, same era.
Universal domain adaptation
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2019
Cited alongside, same era.
Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
Cited alongside, same era.
Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources
Yongchun Zhu, Fuzhen Zhuang, and Deqing Wang · 2019
Cited alongside, same era.
Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus · 2020
Cited alongside, same era.
Bo Fu, Zhangjie Cao, Jianmin Wang, and Mingsheng Long · 2021
Later among the works it cites.
Dynamic transfer for multi-source domain adaptation
Yunsheng Li, Lu Yuan, Yinpeng Chen, Pei Wang, and Nuno Vasconcelos · 2021
Later among the works it cites.
Active domain adaptation via clustering uncertainty-weighted embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman · 2021
Later among the works it cites.
Consensus graph representation learning for better grounded image captioning
Wenqiao Zhang, Haochen Shi, Siliang Tang, Jun Xiao, Qiang Yu, and Yueting Zhuang · 2021
Later among the works it cites.
Madan: multi-source adversarial domain aggregation network for domain adaptation
Sicheng Zhao, Bo Li, Pengfei Xu, Xiangyu Yue, Guiguang Ding, and Kurt Keutzer · 2021
Later among the works it cites.
Domain adaptive ensemble learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
Later among the works it cites.
Evidential neighborhood contrastive learning for universal domain adaptation
Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, and Minghua Deng · 2022
Later among the works it cites.
Active learning for domain adaptation: An energy-based approach
Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, and Guoren Wang · 2022
Later among the works it cites.
Apg: Adaptive parameter generation network for click-through rate prediction
Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Jian Xu, and Bo Zheng · 2022
Later among the works it cites.
Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, and Fei Wu · 2023
Closest in time.
Dirichlet-based uncertainty calibration for active domain adaptation
Mixue Xie, Shuang Li, Rui Zhang, and Chi Harold Liu · 2023
Closest in time.
Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels
Wenqiao Zhang, Changshuo Liu, Lingze Zeng, Bengchin Ooi, Siliang Tang, and Yueting Zhuang · 2023
Closest in time.