A Gating Model for Bias Calibration in Generalized Zero-shot Learning

This work was conducted in the OLIVES @ Georgia Institute of Technology

This is an official code repository for the paper:

Gukyeong Kwon and Ghassan AlRegib, "A Gating Model for Bias Calibration in Generalized Zero-shot Learning," IEEE Transactions on Image Processing, 2022.

If you find our codes useful, we kindly ask you to cite our work.

@article{kwon2022gating,
  title={A Gating Model for Bias Calibration in Generalized Zero-shot Learning},
  author={Kwon, Gukyeong and Al Regib, Ghassan},
  journal={IEEE Transactions on Image Processing},
  year={2022},
  publisher={IEEE}
}

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Getting Started

Set up the environment

Clone this repository and run following commands to create a conda environment and install all dependencies.

conda create -n gating-ae python=3.8
conda activate gating-ae
cd gating-ae
conda install pytorch torchvision -c pytorch
pip install -r requirments.txt

Prepare datasets

GatingAE is evaluated on four datasets which are CUB, SUN, AWA2, and AWA1. The dataset can be downloaded from here, which is prepared by the authors of the CADA-VAE project.

Training and Evaluation of GatingAE

Run following commands:

Use the training split for training

# Train an autoencoder
python train_ae.py --data_path './data' --dataset 'CUB' --save_name 'gating-ae_train'

# Train a seen expert
python train_seen_expert.py --data_path './data' --dataset 'CUB' --save_name 'seen_expert_train'

Use the train + val split for training

# Train an autoencoder
python train_ae.py --data_path './data' --dataset 'CUB' --save_name 'gating-ae_trainval' --use_trainval

# Train a seen expert
python train_seen_expert.py --data_path './data' --dataset 'CUB' --save_name 'seen_expert_trainval' --use_trainval

Trained Weights for CUB

We share the weights of GatingAE trained on CUB for reproducibility. The model weights can be downloaded here. There exist four models: autoencoders and seen experts trained on the train split and the trainval split. There name convention is as follows:

Splits	Autoencoder	Seen expert
train	CUB_gating-ae_train	CUB_seen_expert_train
trainval	CUB_gating-ae_trainval	CUB_seen_expert_trainval

The downloaded models can be unzipped and placed under the save folder for evaluation.

Evaluation

python eval.py \
  --dataset 'CUB' \
  --data_path './data' \
  --val_ae_resume './save/CUB_gating-ae_train/checkpoint.pth' \ 
  --val_clf_resume './save/CUB_seen_expert_train/checkpoint.pth' \
  --trainval_ae_resume './save/CUB_gating-ae_trainval/checkpoint.pth' \ 
  --trainval_clf_resume './save/CUB_seen_expert_trainval/checkpoint.pth'