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Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
On the optimality of conditional expectation as a bregman predictor
Arindam Banerjee, Xin Guo, and Hui Wang · 2005
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Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2007
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Instance weighting for domain adaptation in nlp
Jing Jiang and ChengXiang Zhai · 2007
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 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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A heterogeneous label propagation algorithm for disease gene discovery
Taehyun Hwang and Rui Kuang · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Transfer learning in collaborative filtering for sparsity reduction
Weike Pan, Evan Xiang, Nathan Liu, and Qiang Yang · 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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On pairs of f f -divergences and their joint range
Peter Harremoës and Igor Vajda · 2011
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Transfer learning for activity recognition: A survey
Diane Cook, Kyle D Feuz, and Narayanan C Krishnan · 2013
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Sparse autoencoder-based feature transfer learning for speech emotion recognition
Jun Deng, Zixing Zhang, Erik Marchi, and Björn Schuller · 2013
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Transfer joint matching for unsupervised domain adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 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-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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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
Cited alongside, same era.
Adversarial feature augmentation for unsupervised domain adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese, and Vittorio Murino · 2018
Cited alongside, same era.
Stratified transfer learning for cross-domain activity recognition
Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, and S Yu Philip · 2018
Cited alongside, same era.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
Cited alongside, same era.
Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
Cited alongside, same era.
An information-theoretic approach to transferability in task transfer learning
Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Lizhong Zheng, Amir Zamir, and Leonidas Guibas · 2019
Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks
Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, and Mahdi Soltanolkotabi · 2020
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LEEP: A new measure to evaluate transferability of learned representations
Cuong Nguyen, Tal Hassner, Matthias Seeger, and Cedric Archambeau · 2020
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On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael Jordan, and Chi Jin · 2020
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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A simple feature augmentation for domain generalization
Pan Li, Da Li, Wei Li, Shaogang Gong, Yanwei Fu, and Timothy M Hospedales · 2021
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Cited alongside, same era.
Multi-level semantic feature augmentation for one-shot learning
Zitian Chen, Yanwei Fu, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, and Leonid Sigal · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
A survey of sentiment analysis based on transfer learning
Ruijun Liu, Yuqian Shi, Changjiang Ji, and Ming Jia · 2019
Cited alongside, same era.
Transfer learning in natural language processing
Sebastian Ruder, Matthew E Peters, Swabha Swayamdipta, and Thomas Wolf · 2019
Cited alongside, same era.
Transferability and hardness of supervised classification tasks
Anh T Tran, Cuong V Nguyen, and Tal Hassner · 2019
Cited alongside, same era.
Darec: deep domain adaptation for cross-domain recommendation via transferring rating patterns
Feng Yuan, Lina Yao, and Boualem Benatallah · 2019
Cited alongside, same era.
Mathieu Rosenbaum and Jianfei Zhang · 2021
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OTCE: A transferability metric for cross-domain cross-task representations
Yang Tan, Yang Li, and Shao-Lun Huang · 2021
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A mathematical framework for quantifying transferability in multi-source transfer learning
Xinyi Tong, Xiangxiang Xu, Shao-Lun Huang, and Lizhong Zheng · 2021
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LogME: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Generalization bounds for transfer learning with pretrained classifiers
Tomer Galanti, András György, and Marcus Hutter · 2022
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Frustratingly easy transferability estimation
Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, and Ying Wei · 2022
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Transfer learning for medical image classification: A literature review
Hee E Kim, Alejandro Cosa-Linan, Nandhini Santhanam, Mahboubeh Jannesari, Mate E Maros, and Thomas Ganslandt · 2022
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Generalization bounds for deep transfer learning using majority predictor accuracy
Cuong N Nguyen, Lam Si Tung Ho, Vu Dinh, Tal Hassner, and Cuong V Nguyen · 2022
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Transferability estimation using Bhattacharyya class separability
Michal Pándy, Andrea Agostinelli, Jasper Uijlings, Vittorio Ferrari, and Thomas Mensink · 2022
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Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
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Guan Wang, Yusuke Kikuchi, Jinglin Yi, Qiong Zou, Rui Zhou, and Xin Guo · 2022
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Structured pruning learns compact and accurate models
Mengzhou Xia, Zexuan Zhong, and Danqi Chen · 2022
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