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Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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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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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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Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
Weifeng Ge and Yizhou Yu · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Explicit inductive bias for transfer learning with convolutional networks
Xuhong Li, Yves Grandvalet, and Franck Davoine · 2018
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Rotation equivariant CNNs for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
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Explicit inductive bias for transfer learning with convolutional networks
LI Xuhong, Yves Grandvalet, and Franck Davoine · 2018
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
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Spottune: Transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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The iwildcam 2020 competition dataset
Sara Beery, Elijah Cole, and Arvi Gjoka · 2020
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Supervised contrastive learning for pre-trained language model fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau, and Ves Stoyanov · 2020
Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2021
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Openclip, July 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2020
Cited alongside, same era.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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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 · 2020
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A large-scale study of representation learning with 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, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby · 2020
Cited alongside, same era.
Side-tuning: A baseline for network adaptation via additive side networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Malik · 2020
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FreeLB: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu · 2020
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The evolution of out-of-distribution robustness throughout fine-tuning
Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, and Rebecca Roelofs · 2021
Cited alongside, same era.
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
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Combined scaling for open-vocabulary image classification
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, and Quoc V. Le · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs, 2021
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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Test-time adaptation via conjugate pseudo-labels, 2022
Sachin Goyal, Mingjie Sun, Aditi Raghunathan, and Zico Kolter · 2022
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Caltech 101, Apr 2022
Li, Andreeto, Ranzato, and Perona · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt · 2022
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Contrastive adapters for foundation model group robustness
Michael Zhang and Christopher Ré · 2022
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