https://github.com/JieShibo/PETL-ViT
[ICCV 2023] Binary Adapters, [AAAI 2023] FacT, [Tech report] Convpass
https://github.com/JieShibo/PETL-ViT
parameter-efficient-tuning pytorch transfer-learning visual-prompting
Last synced: over 1 year ago
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[ICCV 2023] Binary Adapters, [AAAI 2023] FacT, [Tech report] Convpass
- Host: GitHub
- URL: https://github.com/JieShibo/PETL-ViT
- Owner: JieShibo
- License: mit
- Created: 2022-07-08T14:31:22.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-08-01T03:12:58.000Z (almost 3 years ago)
- Last Synced: 2024-08-02T15:33:27.599Z (almost 2 years ago)
- Topics: parameter-efficient-tuning, pytorch, transfer-learning, visual-prompting
- Language: Python
- Homepage:
- Size: 18.5 MB
- Stars: 164
- Watchers: 2
- Forks: 7
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Parameter-Efficient Transfer Learning on Vision Transformers
Some exploration of *Parameter-Efficient Transfer Learning* in vision.
+ **Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy**
+ \[ICCV 2023\] [](https://arxiv.org/abs/2307.16867)
[](https://github.com/JieShibo/PETL-ViT/tree/main/binary_adapter)
+ **FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer**
+ \[AAAI 2023 Oral\] [](https://arxiv.org/abs/2212.03145)
[](https://github.com/JieShibo/PETL-ViT/tree/main/FacT)
+ **Convolutional Bypasses Are Better Vision Transformer Adapters**
+ [Tech Report 2022] [](https://arxiv.org/abs/2207.07039)
[](https://github.com/JieShibo/PETL-ViT/tree/main/convpass)