https://github.com/edgarschnfld/CADA-VAE-PyTorch

Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)
https://github.com/edgarschnfld/CADA-VAE-PyTorch

few-shot-learning generalized-zero-shot-learning vae variational-autoencoder zero-shot-learning

Last synced: about 2 months ago
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Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)

• Host: GitHub
• URL: https://github.com/edgarschnfld/CADA-VAE-PyTorch
• Owner: edgarschnfld
• License: mit
• Created: 2019-01-07T19:35:18.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:26:21.000Z (over 1 year ago)
• Last Synced: 2024-07-31T23:45:09.103Z (4 months ago)
• Topics: few-shot-learning, generalized-zero-shot-learning, vae, variational-autoencoder, zero-shot-learning
• Language: Python
• Homepage:
• Size: 39.7 MB
• Stars: 281
• Watchers: 4
• Forks: 58
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-few-shot-meta-learning - code - official (PyTorch)

README

        
# CADA-VAE

Original PyTorch implementation of "Generalized Zero-and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019).

Paper: https://arxiv.org/pdf/1812.01784.pdf



  





  

### Requirements

The code was implemented using Python 3.5.6 and the following packages:

```

torch==0.4.1

numpy==1.14.3

scipy==1.1.0

scikit_learn==0.20.3

networkx==1.11

```

Using Python 2 is not recommended.

### Data

Download the following folder https://www.dropbox.com/sh/btoc495ytfbnbat/AAAaurkoKnnk0uV-swgF-gdSa?dl=0

and put it in this repository.

Next to the folder "model", there should be a folder "data".

### Experiments

To run the experiments from the paper, navigate to the model folder and execute the following:

```

python single_experiment.py --dataset CUB --num_shots 0 --generalized True

```

The choices for the input arguments are:

```

datasets: CUB, SUN, AWA1, AWA2

num_shots: any number 

generalized: True, False

```

More hyperparameters can be adjusted in the file single_experiment.py directly. The results vary by 1-2% between identical runs.

### Citation

If you use this work please cite

```

@inproceedings{schonfeld2019generalized,

  title={Generalized zero-and few-shot learning via aligned variational autoencoders},

  author={Schonfeld, Edgar and Ebrahimi, Sayna and Sinha, Samarth and Darrell, Trevor and Akata, Zeynep},

  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},

  pages={8247--8255},

  year={2019}

}

```

### Contact 

For questions or help, feel welcome to write an email to [email protected]