Dynamic-Representation-Enhancement-framework

The source code for the paper "learning general features to bridge the cross-domain gaps in few-shot learning."

Note

• This code is referenced ATA and FWT.
• The dataset, model, and code are for non-commercial research purposes only.

Prerequisites

• Python >= 3.5
• Pytorch >= 1.2.0
• You can use the requirements.txt file we provide to setup the environment via Anaconda.

conda create --name py38 python=3.8
conda install pytorch torchvision -c pytorch
pip3 install -r requirements.txt

Datasets

Please refer to CDFSL-ATA (https://github.com/Haoqing-Wang/CDFSL-ATA).

Train

1. Train the baseline.

python train.py --model ResNet10 --method GNN --n_shot 1 --name baseline_1s
python train.py --model ResNet10 --method GNN --n_shot 5 --name baseline_5s

2. Train the model with the proposed DRE framework.

python train.py --model ResNet10_mask --method GNN --name GNN_ml_1s --n_shot 1 --rsc True --lifted_struct_loss True
python train.py --model ResNet10_mask --method GNN --name GNN_ml_5s --n_shot 5 --rsc True --lifted_struct_loss True