GRaWD

The official implementation of Generative Random Walk Deviation Loss for Improved Unseen Learning Representation. ICCV 2021 under review.

Requirements

Packages

Python 3.6, Pytorch 1.6, sklearn, scipy, matplotlib, random, copy and other general packages

Processed Feature Data

You can download the text-based dataset at dataset CUBird and NABird. For attribute-based data, you can access to here.

Please put the uncompressed data to the folder "data".

Training and Testing

We provide integrated code for training and testing.

Training on text-based datasets

cd 'your main folder'

python train.py --dataset CUB --splitmode easy --exp_name 'CUB_easy_Rep' --rw_config_path ./configs/CUB_easy_Best_HPs.yml
python train.py --dataset CUB --splitmode hard --exp_name 'CUB_hard_Rep' --rw_config_path ./configs/CUB_hard_Best_HPs.yml
python train.py --dataset NAB --splitmode easy --exp_name 'NAB_easy_Rep' --rw_config_path ./configs/NAB_easy_Best_HPs.yml
python train.py --dataset NAB --splitmode hard --exp_name 'NAB_hard_Rep' --rw_config_path ./configs/NAB_hard_Best_HPs.yml

Training on attribute-based datasets

cd 'your main folder'

#python train_GBU.py --dataset APY --preprocessing --exp_name 'aPY_rep' --rw_config_path ./configs/aPY_Best_HPs.yml
#python train_GBU.py --dataset AWA2 --preprocessing --exp_name 'awa2_rep' --rw_config_path ./configs/AWA2_Best_HPs.yml
#python train_GBU.py --dataset SUN --preprocessing --exp_name 'sun_rep' --rw_config_path ./configs/SUN_Best_HPs.yml

Here we provide the potential best hyper-parameters that can reproduce our reported results. You can refer to the logs under main folder for the training details. For each trial, the final performance may vary a little bit. Following standard setting, we report the best performance after k-trials.

Reference

• Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng and Ahmed Elgammal "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts", CVPR, 2018
• Mohamed Elhoseiny, Mohamed Elfeki, Creativity Inspired Zero Shot Learning, Thirty-sixth International Conference on Computer Vision (ICCV), 2019
• Elhoseiny, Mohamed, Kai Yi, and Mohamed Elfeki. "CIZSL++: Creativity Inspired Generative Zero-Shot Learning." arXiv preprint arXiv:2101.00173 (2021).

If you find this code is useful, please cite:

@article{grawd,
  title={Generative Random Walk Deviation Loss for Improved Unseen Learning Representation},
  author={Divyansh, Jha and Kai, Yi and Ivan Skorokhodov, Ivan and Elhoseiny, Mohamed},
  journal={CVPR 2021 under review},
  year={2021}
}