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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

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[ICCV 2023] Binary Adapters, [AAAI 2023] FacT, [Tech report] Convpass

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# 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**
+ \[ICCV 2023\] [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=Binary_Adapter&color=red&logo=arxiv)](https://arxiv.org/abs/2307.16867)
[![Github link](https://img.shields.io/static/v1?label=GitHub&message=Binary_Adapter&color=black&logo=github)](https://github.com/JieShibo/PETL-ViT/tree/main/binary_adapter)

+ **FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer**
+ \[AAAI 2023 Oral\] [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=FacT&color=red&logo=arxiv)](https://arxiv.org/abs/2212.03145)
[![Github link](https://img.shields.io/static/v1?label=GitHub&message=FacT&color=black&logo=github)](https://github.com/JieShibo/PETL-ViT/tree/main/FacT)

+ **Convolutional Bypasses Are Better Vision Transformer Adapters**
+ [Tech Report 2022] [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=Convpass&color=red&logo=arxiv)](https://arxiv.org/abs/2207.07039)
[![Github link](https://img.shields.io/static/v1?label=GitHub&message=Convpass&color=black&logo=github)](https://github.com/JieShibo/PETL-ViT/tree/main/convpass)