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Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is essential to take advantage of plentiful model resources.
A new measure of rank correlation
Maurice G Kendall · 1938
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A reasonable social welfare function
Ann Arbor · 1951
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The copeland method: I.: Relationships and the dictionary
Saari, Donald G., and Vincent R. Merlin · 1996
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Optimizing search engines using clickthrough data
Thorsten Joachims · 2002
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Exploiting task relatedness for multiple task learning
Shai Ben-David and Reba Schuller · 2003
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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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Learning methods for generic object recognition with invariance to pose and lighting
Yann LeCun, Fu Jie Huang, and Léon Bottou · 2004
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To transfer or not to transfer
Michael T Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G Dietterich · 2005
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Representational similarity analysis – connecting the branches of systems neuroscience
Nikolaus Kriegeskorte · 2008
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Listwise approach to learning to rank: theory and algorithm
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Preference-based learning to rank
Nir Ailon and Mehryar Mohri · 2010
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Metric learning to rank
Brian McFee and Gert R. G. Lanckriet · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Y. Ng, and Honglak Lee · 2011
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Novel dataset for fine-grained image categorization: Stanford dogs
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N. Rockmore · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, 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 Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, and Serge J. Belongie · 2015
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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
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep cross residual learning for multitask visual recognition
Brendan Jou and Shih-Fu Chang · 2016
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Transferability and hardness of supervised classification tasks
Anh Tuan Tran, Cuong V. Nguyen, and Tal Hassner · 2019
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Characterizing and avoiding negative transfer
Zirui Wang, Zihang Dai, Barnabás Póczos, and Jaime Carbonell · 2019
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Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolò Fusi · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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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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Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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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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Aid: A benchmark dataset for performance evaluation of aerial scene classification
Gui-Song Xia, Jingwen Hu, Fan Hu, Baoguang Shi, Xiang Bai, Yanfei Zhong, and Liangpei Zhang · 2016
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Learnware: on the future of machine learning
Zhi-Hua Zhou · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
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dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Cited alongside, same era.
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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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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Scalable diverse model selection for accessible transfer learning
Daniel Bolya, Rohit Mittapalli, and Judy Hoffman · 2021
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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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Shibal Ibrahim, Natalia Ponomareva, and Rahul Mazumder · 2021
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Ranking neural checkpoints
Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, and Boqing Gong · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 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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Logme: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long · 2021
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GULP: a prediction-based metric between representations
Enric Boix Adserà, Hannah Lawrence, George Stepaniants, and Philippe Rigollet · 2022
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How stable are transferability metrics evaluations?
Andrea Agostinelli, Michal Pándy, Jasper R. R. Uijlings, Thomas Mensink, and Vittorio Ferrari · 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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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge J. Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Efficient self-supervised vision transformers for representation learning
Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, and Jianfeng Gao · 2022
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Transferability estimation using bhattacharyya class separability
Michal Pándy, Andrea Agostinelli, Jasper R. R. Uijlings, Vittorio Ferrari, and Thomas Mensink · 2022
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Transferability estimation based on principal gradient expectation
Huiyan Qi, Lechao Cheng, Jingjing Chen, Yue Yu, Zunlei Feng, and Yu-Gang Jiang · 2022
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Which model to transfer? finding the needle in the growing haystack
Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, and Mario Lucic · 2022
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Not all models are equal: Predicting model transferability in a self-challenging fisher space
Wenqi Shao, Xun Zhao, Yixiao Ge, Zhaoyang Zhang, Lei Yang, Xiaogang Wang, Ying Shan, and Ping Luo · 2022
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Ranking and tuning pre-trained models: A new paradigm for exploiting model hubs
Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I Jordan, and Mingsheng Long · 2022
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Learnware: Small models do big
Zhi-Hua Zhou and Zhi-Hao Tan · 2022
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