edgarschnfld / Cada Vae Pytorch
Licence: mit
Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)
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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
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