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Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only tunes a small number of parameters while freezing the vast majority ones to ease storage burden and optimization difficulty.
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Attention is all you need
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A large-scale study of representation learning with the visual task adaptation benchmark
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A large-scale study of representation learning with the visual task adaptation benchmark
Intriguing properties of vision transformers
M. M. Naseer, K. Ranasinghe, S. H. Khan, M. Hayat, F. Shahbaz Khan, and M.-H. Yang · 2021
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Do vision transformers see like convolutional neural networks?
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
H. Wu, J. Xu, J. Wang, and M. Long · 2021
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Raise a child in large language model: Towards effective and generalizable fine-tuning
R. Xu, F. Luo, Z. Zhang, C. Tan, B. Chang, S. Huang, and F. Huang · 2021
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Vitae: Vision transformer advanced by exploring intrinsic inductive bias
Y. Xu, Q. Zhang, J. Zhang, and D. Tao · 2021
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X. Zhai, J. Puigcerver, A. Kolesnikov, P. Ruyssen, C. Riquelme, M. Lucic, J. Djolonga, A. S. Pinto, M. Neumann, A. Dosovitskiy, et al · 2019
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C. Zhang, S. Bengio, and Y. Singer · 2019
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Once-for-all: Train one network and specialize it for efficient deployment
H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han · 2020
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Tinytl: Reduce memory, not parameters for efficient on-device learning
H. Cai, C. Gan, L. Zhu, and S. Han · 2020
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The intriguing role of module criticality in the generalization of deep networks
N. S. Chatterji, B. Neyshabur, and H. Sedghi · 2020
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Adafilter: Adaptive filter fine-tuning for deep transfer learning
Y. Guo, Y. Li, L. Wang, and T. Rosing · 2020
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What is being transferred in transfer learning?
B. Neyshabur, H. Sedghi, and C. Zhang · 2020
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S. Chen, C. Ge, Z. Tong, J. Wang, Y. Song, J. Wang, and P. Luo · 2022
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Towards a unified view of parameter-efficient transfer learning
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Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2022
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LoRA: Low-rank adaptation of large language models
E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2022
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Visual prompt tuning
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim · 2022
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Convolutional bypasses are better vision transformer adapters
S. Jie and Z.-H. Deng · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
A. Kumar, A. Raghunathan, R. M. Jones, T. Ma, and P. Liang · 2022
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Scaling & shifting your features: A new baseline for efficient model tuning
D. Lian, D. Zhou, J. Feng, and X. Wang · 2022
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On-device training under 256kb memory
J. Lin, L. Zhu, W.-M. Chen, W.-C. Wang, C. Gan, and S. Han · 2022
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A convnet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
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Deep transfer learning for image classification: a survey
J. Plested and T. Gedeon · 2022
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Lst: Ladder side-tuning for parameter and memory efficient transfer learning
Y.-L. Sung, J. Cho, and M. Bansal · 2022
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
E. B. Zaken, Y. Goldberg, and S. Ravfogel · 2022
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Y. Zhang, K. Zhou, and Z. Liu · 2022
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Consolidator: Mergable adapter with group connections for vision transformer
T. Hao, H. Chen, Y. Guo, and G. Ding · 2023
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Visual query tuning: Towards effective usage of intermediate representations for parameter and memory efficient transfer learning
C.-H. Tu, Z. Mai, and W.-L. Chao · 2023
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