This project's objective is to reproduce the results from
https://static.googleusercontent.com/media/research.google.com/pt-BR//pubs/archive/41473.pdf
However, we use a smaller dataset (CIFAR-100) with lower resolution images. In order to do so, new cost functions needed to be created.
To use this code
- Get CIFAR-10 and CIFAR-100 from:
https://www.cs.toronto.edu/~kriz/cifar.html
Put cifar-100 test, train and meta files inside a folder called cifar-100-python/ (read_cifar100 conde uses them)
- Download the glove model from
http://nlp.stanford.edu/data/glove.6B.zip
And extract the files into a folder called glove.6B
- Install python dependencies by using pip and the requirements.txt file:
sudo pip install requirements.txt
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Create a folder called pickle files and run read_cifar100 to create all datasets
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To train the composite model, run the train_composite file
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To visualize the TSNE plots, run the visualize_results file (Change the indicated vars on the code)
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To compute quantitative results, run the compute_quantitative_results file and use the functions (Change the indicated vars on the code)