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In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods.
Adaptation of maximum entropy capitalizer: Little data can help a lot
Ciprian Chelba and Alex Acero · 2006
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Discriminative learning for differing training and test distributions
Steffen Bickel, Michael Brückner, and Tobias Scheffer · 2007
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Boosting for transfer learning
Wenyuan Dai, Qiang Yang, Gui-Rong Xue, and Yong Yu · 2007
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Frustratingly easy domain adaptation
Hal Daumé III · 2007
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, and Alex J. Smola · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, and Motoaki Kawanabe · 2007
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A least-squares approach to direct importance estimation
Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2010
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Boosting for regression transfer
David Pardoe and Peter Stone · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Feature selection for transfer learning
Selen Uguroglu and Jaime Carbonell · 2011
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Nearest neighbor-based importance weighting
Marco Loog · 2012
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Unsupervised visual domain adaptation using subspace alignment
Basura Fernando, Amaury Habrard, Marc Sebban, and Tinne Tuytelaars · 2013
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Relative density-ratio estimation for robust distribution comparison
Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, and Masashi Sugiyama · 2013
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 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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Understanding the mechanisms of deep transfer learning for medical images
Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani, Sheshadri Thiruvenkadam, Pavan Annangi, Narayanan Babu, and Vivek Vaidya · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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A survey of transfer learning
Karl Weiss, Taghi M. Khoshgoftaar, and DingDing Wang · 2016
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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Bridging theory and algorithm for domain adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan · 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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Transfer learning toolkit: Primers and benchmarks, 2019
Fuzhen Zhuang, Keyu Duan, Tongjia Guo, Yongchun Zhu, Dongbo Xi, Zhiyuan Qi, and Qing He · 2019
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Adversarial weighting for domain adaptation in regression
Antoine de Mathelin, Guillaume Richard, Francois Deheeger, Mathilde Mougeot, and Nicolas Vayatis · 2020
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Domain adaptation toolbox, 2016
Ke Yan · 2016
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 2017
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Learn on source, refine on target: A model transfer learning framework with random forests
N. Segev, M. Harel, S. Mannor, K. Crammer, and R. El-Yaniv · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep domain adaptation networks, 2018
Erlend Davidson · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, and Michael I Jordan · 2018
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Transfer-learning-library
Mingsheng Long Junguang Jiang, Bo Fu · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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(yet) another domain adaptation library, 2020
Anne-Marie Tousch and Christophe Renaudin · 2020
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Transfertools, 2020
V Vercruyssen · 2020
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Adapt: Awesome domain adaptation python toolbox
Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, and Nicolas Vayatis · 2021
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Unsupervised domain adaptation for constraining star formation histories
Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt, and Guillaume Richard · 2021
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Handling distribution shift in tire design
Antoine De Mathelin, François Deheeger, Mathilde Mougeot, and Nicolas Vayatis · 2021
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Pytorch-adapt
Kevin Musgrave · 2021
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Domain adaptive ensemble learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
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Fast and accurate importance weighting for correcting sample bias
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, and Nicolas Vayatis · 2022
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