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Pre-training & fine-tuning is a prevalent paradigm in computer vision (CV).
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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Training deep nets with sublinear memory cost. corr abs/1604.06174 (2016)
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Fine-tuning deep neural networks in continuous learning scenarios
Christoph Käding, Erik Rodner, Alexander Freytag, and Joachim Denzler · 2016
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
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Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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To tune or not to tune? adapting pretrained representations to diverse tasks
Matthew E Peters, Sebastian Ruder, and Noah A Smith · 2019
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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A comparative study of fine-tuning deep learning models for plant disease identification
Edna Chebet Too, Li Yujian, Sam Njuki, and Liu Yingchun · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith · 2020
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Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend
Fatsuma Jauro, Haruna Chiroma, Abdulsalam Y Gital, Mubarak Almutairi, M Abdulhamid Shafi’i, and Jemal H Abawajy · 2020
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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, Peter J Liu, et al · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer · 2021
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Fine-tuning deep learning models for pedestrian detection
Caisse Amisse, Mario Ernesto Jijón-Palma, and Jorge Antonio Silva Centeno · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Convolutional bypasses are better vision transformer adapters
Shibo Jie and Zhi-Hong Deng · 2022
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Prompting visual-language models for efficient video understanding
Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie · 2022
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Learning common and specific visual prompts for domain generalization
Aodi Li, Liansheng Zhuang, Shuo Fan, and Shafei Wang · 2022
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Pro-tuning: Unified prompt tuning for vision tasks
Xing Nie, Bolin Ni, Jianlong Chang, Gaomeng Meng, Chunlei Huo, Zhaoxiang Zhang, Shiming Xiang, Qi Tian, and Chunhong Pan · 2022
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St-adapter: Parameter-efficient image-to-video transfer learning
Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li · 2022
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Cited alongside, same era.
Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
Cited alongside, same era.
Adapterfusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked languagemodels
Shauli Ravfogel, Elad Ben-Zaken, and Yoav Goldberg · 2021
Cited alongside, same era.
Adapterdrop: On the efficiency of adapters in transformers
Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, and Iryna Gurevych · 2021
Cited alongside, same era.
Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge
Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf, Georg Rose, Andreas Nürnberger, and Oliver Speck · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
Cited alongside, same era.
Eva: Exploring the limits of masked visual representation learning at scale
Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2022
Cited alongside, same era.
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al · 2022
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Visual prompt tuning for generative transfer learning
Kihyuk Sohn, Yuan Hao, José Lezama, Luisa Polania, Huiwen Chang, Han Zhang, Irfan Essa, and Lu Jiang · 2022
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Lst: Ladder side-tuning for parameter and memory efficient transfer learning
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
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Focal modulation networks
Jianwei Yang, Chunyuan Li, Xiyang Dai, and Jianfeng Gao · 2022
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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Detrs with collaborative hybrid assignments training
Zhuofan Zong, Guanglu Song, and Yu Liu · 2022
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Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery
Xian Sun, Dongshuo Yin, Fei Qin, Hongfeng Yu, Wanxuan Lu, Fanglong Yao, Qibin He, Xingliang Huang, Zhiyuan Yan, Peijin Wang, et al · 2023
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Lion: Implicit vision prompt tuning
Haixin Wang, Jianlong Chang, Xiao Luo, Jinan Sun, Zhouchen Lin, and Qi Tian · 2023
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