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Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline.
Asymptotic slowing down of the nearest-neighbor classifier
Robert R Snapp, Demetri Psaltis, and Santosh S Venkatesh · 1991
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Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2006
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
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CNN features off-the-shelf: An astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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What makes ImageNet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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Rethinking the Inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
Weifeng Ge and Yizhou Yu · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
Cited alongside, same era.
Domain adaptive transfer learning with specialist models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V Le, and Ruoming Pang · 2018
Cited alongside, same era.
A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
Cited alongside, same era.
Do better Imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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Transferability and hardness of supervised classification tasks
Anh T Tran, Cuong V Nguyen, and Tal Hassner · 2019
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The Visual Task Adaptation Benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
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P2l: Predicting transfer learning for images and semantic relations
Bishwaranjan Bhattacharjee, John R Kender, Matthew Hill, Parijat Dube, Siyu Huo, Michael R Glass, Brian Belgodere, Sharath Pankanti, Noel Codella, and Patrick Watson · 2020
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Theoretical guarantees of transfer learning
Zirui Wang · 2018
Cited alongside, same era.
Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Cited alongside, same era.
Task2vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C Fowlkes, Stefano Soatto, and Pietro Perona · 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
Cited alongside, same era.
Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
Cited alongside, same era.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
Cited alongside, same era.
Duality diagram similarity: a generic framework for initialization selection in task transfer learning
Kshitij Dwivedi, Jiahui Huang, Radoslaw Martin Cichy, and Gemma Roig · 2020
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Source model selection for deep learning in the time series domain
Amiel Meiseles and Lior Rokach · 2020
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Leep: A new measure to evaluate transferability of learned representations
Cuong V Nguyen, Tal Hassner, Cedric Archambeau, and Matthias Seeger · 2020
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Depara: Deep attribution graph for deep knowledge transferability
Jie Song, Yixin Chen, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, and Mingli Song · 2020
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A linearized framework and a new benchmark for model selection for fine-tuning
Aditya Deshpande, Alessandro Achille, Avinash Ravichandran, Hao Li, Luca Zancato, Charless Fowlkes, Rahul Bhotika, Stefano Soatto, and Pietro Perona · 2021
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Factors of influence for transfer learning across diverse appearance domains and task types
Thomas Mensink, Jasper Uijlings, Alina Kuznetsova, Michael Gygli, and Vittorio Ferrari · 2021
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Scalable transfer learning with expert models
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cedric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, and Neil Houlsby · 2021
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