noah's Introduction

Neural Prompt Search

S-Lab, Nanyang Technological University

TL;DR

The idea is simple: we view existing parameter-efficient tuning modules, including Adapter, LoRA and VPT, as prompt modules and propose to search the optimal configuration via neural architecture search. Our approach is named NOAH (Neural prOmpt seArcH).

[arXiv] • [project page]

Updatas

[05/2022] arXiv paper has been released.

Environment Setup

conda create -n NOAH python=3.8
conda activate NOAH
pip install -r requirements.txt

Data Preparation

1. Visual Task Adaptation Benchmark (VTAB)

cd data/vtab-source
python get_vtab1k.py

2. Few-Shot and Domain Generation

Images

Please refer to DATASETS.md to download the datasets.
Train/Val/Test splits

Please refer to files under data/XXX/XXX/annotations for the detail information.

Quick Start For NOAH

We use the VTAB experiments as examples.

1. Downloading the Pre-trained Model

Model	Link
ViT B/16	link

2. Supernet Training

sh configs/NOAH/VTAB/supernet/slurm_train_vtab.sh PATH-TO-YOUR-PRETRAINED-MODEL

3. Subnet Search

sh configs/NOAH/VTAB/search/slurm_search_vtab.sh PARAMETERS-LIMITES

4. Subnet Retraining

sh configs/NOAH/VTAB/subnet/slurm_retrain_vtab.sh PATH-TO-YOUR-PRETRAINED-MODEL

We add the optimal subnet architecture of each dataset in the experiments/NOAH/subnet/VTAB.

5. Performance

Citation

If you use this code in your research, please kindly cite this work.

@inproceedings{zhang2022NOAH,
      title={Neural Prompt Search}, 
      author={Yuanhan Zhang and Kaiyang Zhou and Ziwei Liu},
      year={2022},
      archivePrefix={arXiv},
}