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A Cross-modal Embedding Approach by Utilizing VAEGANs on Generalized Zero-Shot Learning

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Aligned-VAEGAN

A Cross-modal Embedding Approach by Utilizing VAEGANs on Generalized Zero-Shot Learning

saved parameters can be downloaded here https://drive.google.com/file/d/1ziUTlGeRAAfYELFDSZYrg2fD3DkbNXbz/view?usp=sharing

put the folder in the directory with the other files

Dataset

Download the following folder https://www.dropbox.com/sh/rf9vlodd49svetp/AAC62VPE-rHgvodQ0bFTopGua?dl=0 and put it in this repository. Next to the folder "model", there should be a folder "data". Raw data for:

  1. AWA2 - https://cvml.ist.ac.at/AwA2/
  2. CUB - http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
  3. SUN - http://cs.brown.edu/~gmpatter/sunattributes.html (check the bottom of the site page)

Package Requirement:

  • python==3.6
  • torch==0.4.1
  • numpy==1.14.3
  • scipy==1.1.0
  • scikit_learn==0.20.3
  • networkx==1.11
  • tensorboardX==1.9
  • tensorflow==2.0.0

Testing Command

To try the model with pretrained parameters, download the saved parameters above, and type this command in the terminal

python single_experiment.py --dataset AWA2 --num_gen_iter 5 --num_dis_iter 4 --mod_dataset 5Gen4Dis --device cuda --pretrain True

another arguments and their explanation can be found in "single_experiment.py"

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A Cross-modal Embedding Approach by Utilizing VAEGANs on Generalized Zero-Shot Learning

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