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ZSL_ABP

This is a PyTorch implementation for the conference paper:

Yizhe Zhu, Jianwen Xie , Bingchen Liu, Ahmed Elgammal "Learning Feature-to-Feature Translator by Alternating Back-Propagation for Zero-Shot Learning", ICCV, 2019

Requirements

  • Python 3
  • pytorch 1.0

Results evaluated on GBU setting

Download the data and uncompress it to the folder 'data/'.

To train the model, run the following command. CUB datset:

python train_ABP.py --dataset CUB --z_dim 10 --sigma 0.3 --langevin_s 0.1 --langevin_step 5   --batchsize 64 --nSample 300

AWA1 datset:

python train_ABP.py --dataset AWA1 --z_dim 10 --sigma 0.3 --langevin_s 0.1 --langevin_step 5   --batchsize 64 --nSample 1500

AWA2 datset:

python train_ABP.py --dataset AWA2 --z_dim 10 --sigma 0.3 --langevin_s 0.1 --langevin_step 5   --batchsize 64 --nSample 1500

SUN datset:

python train_ABP.py --dataset SUN  --z_dim 10 --sigma 0.3 --langevin_s 0.1 --langevin_step 5   --batchsize 64 --nSample 300 

Citation

If you use this code in your research, please consider citing:

@InProceedings{zhu2019learning,
    title={Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning},
    author={Zhu, Yizhe and Xie, Jianwen and Liu, Bingchen and Elgammal, Ahmed},
    booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
    month = {Oct},
    year = {2019}
}

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Code for the paper ICCV'19 "Learning Feature-to-Feature Translator by Alternating Back-Propagation for Zero-Shot Learning"

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