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Trainable Projected Gradient Method (TPGM)
This repo implements the experiments in the paper Trainable Projected Gradient Method for Robust Fine-Tuning
. Specifically, we divde experiments into two categories: DomainNet experiments using ResNet and ImageNet experiments using ViT. The main difference is in how TPGM is applied.
Create conda environment
- The environment uses Pytorch 1.7 supported on CUDA 11.x and python 3.8.
cd TPGM
conda env create -f environment.yml
conda activate py38
DomainNet using experiments using ResNet
- The code resides in the folder
DomainNet_ResNet_Exp
. - Following the paper, TPGM is used at every iteration of fine-tuning.
ImageNet experiments using ViT
- The code resides in the folder
ImageNet_ViT_Exp
. - Following the paper, TPGM is only used at the end of fine-tuning.