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Code for A Generative Approach to Zero-Shot learning Using Conditonal Variational Autoencoders

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CVAE_ZSL

You need keras with tensorflow backend to run this code. To train the SVM model, you also need sklearn

Download the Data

  • You can download the CUB data from this link

  • Unzip the file and place the 'CUB' folder in Datasets/

To partition the data into test and train classes

cd Disjoint/CUB/
python testTrainSplit.py

To train the CVAE model and run the subsequent supervised SVM classifier.

python trainCVAE.py

If you find our code useful, please cite our work:

@InProceedings{Mishra_2018_CVPR_Workshops,
author = {Mishra, Ashish and Krishna Reddy, Shiva and Mittal, Anurag and Murthy, Hema A.},
title = {A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
} 

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Code for A Generative Approach to Zero-Shot learning Using Conditonal Variational Autoencoders

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