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 edgarschoenfeld@live.de