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It is commonly believed that in transfer learning including more pre-training data translates into better performance.
“Residuals and influence in regression”
R Cook and Sanford Weisberg · 1982
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“WordNet: a lexical database for English”
George Miller · 1995
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“Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories”
Li Fei-Fei, Rob Fergus and Pietro Perona · 2004
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“Caltech-256 object category dataset”
Gregory Griffin, Alex Holub and Pietro Perona · 2007
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“Automated flower classification over a large number of classes”
Maria-Elena Nilsback and Andrew Zisserman · 2008
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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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“Learning Multiple Layers of Features from Tiny Images”
Alex Krizhevsky · 2009
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“Sun database: Large-scale scene recognition from abbey to zoo”
Jianxiong Xiao, James Hays, Krista Ehinger, Aude Oliva and Antonio Torralba · 2010
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“Robust statistics: the approach based on influence functions”
Frank Hampel, Elvezio Ronchetti, Peter Rousseeuw and Werner Stahel · 2011
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“Cats and dogs”
Omkar Parkhi, Andrea Vedaldi, Andrew Zisserman and CV Jawahar · 2012
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“Collecting a large-scale dataset of fine-grained cars”, 2013
Jonathan Krause, Jia Deng, Michael Stark and Li Fei-Fei · 2013
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“Fine-grained visual classification of aircraft”
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko and Andrea Vedaldi · 2013
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“Food-101–mining discriminative components with random forests”
Lukas Bossard, Matthieu Guillaumin and Luc Van · 2014
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“Birdsnap: Large-scale fine-grained visual categorization of birds”
Thomas Berg, Jiongxin Liu, Seung Woo, Michelle Alexander, David Jacobs and Peter Belhumeur · 2014
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“Rich feature hierarchies for accurate object detection and semantic segmentation”
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik · 2014
Earlier work this paper cites.
“Factors of transferability for a generic convnet representation”
Hossein Azizpour, Ali Razavian, Josephine Sullivan, Atsuto Maki and Stefan Carlsson · 2015
Cited alongside, same era.
“Faster r-cnn: Towards real-time object detection with region proposal networks”
Shaoqing Ren, Kaiming He, Ross Girshick and Jian Sun · 2015
Cited alongside, same era.
“R-fcn: Object detection via region-based fully convolutional networks”
Jifeng Dai, Yi Li, Kaiming He and Jian Sun · 2016
Cited alongside, same era.
“What makes ImageNet good for transfer learning?”
Minyoung Huh, Pulkit Agrawal and Alexei Efros · 2016
Cited alongside, same era.
“Transfer learning from deep features for remote sensing and poverty mapping”
Michael Xie, Neal Jean, Marshall Burke, David Lobell and Stefano Ermon · 2016
Cited alongside, same era.
“Data shapley: Equitable valuation of data for machine learning”
Amirata Ghorbani and James Zou · 2019
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“Learning what and where to transfer”
Yunhun Jang, Hankook Lee, Sung Hwang and Jinwoo Shin · 2019
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“Big Transfer (BiT): General Visual Representation Learning”
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly and Neil Houlsby · 2019
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“Do better imagenet models transfer better?”
Simon Kornblith, Jonathon Shlens and Quoc Le · 2019
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“Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques”
Sherrie Wang, George Azzari and David Lobell · 2019
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“Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs”
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy and Alan Yuille · 2017
Cited alongside, same era.
“Understanding Black-box Predictions via Influence Functions”
Pang Koh and Percy Liang · 2017
Cited alongside, same era.
“End-to-end ego lane estimation based on sequential transfer learning for self-driving cars”
Jiman Kim and Chanjong Park · 2017
Cited alongside, same era.
“Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases”
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri and Ronald Summers · 2017
Cited alongside, same era.
“Panning for gold:‘model-X’knockoffs for high dimensional controlled variable selection”
Emmanuel Candes, Yingying Fan, Lucas Janson and Jinchi Lv · 2018
Cited alongside, same era.
“Senteval: An evaluation toolkit for universal sentence representations”
Alexis Conneau and Douwe Kiela · 2018
Cited alongside, same era.
“Comparison of deep transfer learning strategies for digital pathology”
Romain Mormont, Pierre Geurts and Raphaël Marée · 2018
Cited alongside, same era.
Vitaly Feldman and Chiyuan Zhang · 2020
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“What is being transferred in transfer learning?”
Behnam Neyshabur, Hanie Sedghi and Chiyuan Zhang · 2020
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“Do Adversarially Robust ImageNet Models Transfer Better?”
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor and Aleksander Madry · 2020
Later among the works it cites.
“Adversarially-Trained Deep Nets Transfer Better”
Francisco Utrera, Evan Kravitz, N. Erichson, Rajiv Khanna and Michael. Mahoney · 2020
Later among the works it cites.
“CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation”
Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Ng and Pranav Rajpurkar · 2021
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“Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning”
Yongchan Kwon and James Zou · 2021
Later among the works it cites.
“Datamodels: Predicting Predictions from Training Data”
Andrew Ilyas, Sung Park, Logan Engstrom, Guillaume Leclerc and Aleksander Madry · 2022
Closest in time.
“Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines”
Bojan Karlaš, David Dao, Matteo Interlandi, Bo Li, Sebastian Schelter, Wentao Wu and Ce Zhang · 2022
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“Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution”
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma and Percy Liang · 2022
Closest in time.
“ffcv”, https://github.com/libffcv/ffcv/ , 2022
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Park, Hadi Salman and Aleksander Madry · 2022
Closest in time.