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An implementation for the paper: Co-Representation Network for Generalized Zero-Shot Learning
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APY_evaluate.py
APY_train.py
AwA2_evaluate.py
AwA2_train.py
CUB_evaluate.py
CUB_train.py
README.md
SUN_evaluate.py
SUN_train.py
net.py
utils.py

README.md

CRnet

An implementation for the paper: Co-Representation Network for Generalized Zero-Shot Learning

Version 1.01

python3.6, pytorch0.4 required

Dataset

The code uses the ResNet101 features provided by the paper: Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly, and follows its GZSL settings.

The features can be download here: http://datasets.d2.mpi-inf.mpg.de/xian/xlsa17.zip

Training

"python CUB_train.py" to train the model.

"python CUB_evaluate.py" to test.

  • Loss starts to decrease at approximately episode 30000.

Trained models can be downloaded from https://drive.google.com/open?id=1a1cAG1iOZQAUPoxLG96_xZiW21Him4wS

Ciation

If you find this work useful for your project, cite:

@inproceedings{zhang2019co,
  title={Co-Representation Network for Generalized Zero-Shot Learning},
  author={Zhang, Fei and Shi, Guangming},
  booktitle={International Conference on Machine Learning},
  pages={7434--7443},
  year={2019}
}
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