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seq2seq go public Feb 4, 2019
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__init__.py
end2end_bimodal.py go public Feb 4, 2019
end2end_trimodal.py go public Feb 4, 2019
readme.md

readme.md

This repo implements the Multimodal Cyclic Translation Network (MCTN) model for the following paper:

Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities

Hai Pham*, Paul Pu Liang*, Thomas Manzini, Louis-Philippe Morency, Barnabás Poczós. AAAI 2019.

Installation

You need to have numpy and the following standard packages which can also be easily installed using pip.

tensorflow>=1.4.0

keras>=2.1.2

recurrentshop

We also need to mention that the seq2seq code is extended from the following github: https://github.com/farizrahman4u/seq2seq. We are grateful for this great repo which really helped us in speeding up our implementation and experiments.

Usage

First add the current directory to the $PYTHONPATH by source set_up. Second, you need to process your data according to the instruction in the function utils/data_loader .py/load_and_preprocess_data. To use the code directly, you need a 3D Numpy array for each modality. After that:

For running the Bimodal MCTN:

$ python enc2end_bimodal.py \ 
    --train_epoch 200 \ 
    --batch_size 32 \ 
    --feature t f \ 
    --cfg configs/mctn.yaml 

For running the Trimodal (Hierarchical) MCTN:

$ python enc2end_trimodal.py \ 
    --train_epoch 200 \ 
    --batch_size 32 \ 
    --feature t f c \ 
    --cfg configs/hierarchical_mctn.yaml 

Note that you can also run those scripts directly with our default arguments. For changing those arguments, please refer to args.py in the utils package for general arguments. For architecture specific settings, please extend from the sample configuration files in the configs directory. Furthermore, you can easily follow our standard models in the packages models to design new architecture for your specific use case.

License

Standard GPL License. See the LICENSE file for more detail.

Copyright 2019 Hai Pham.

Citation

If you use any part of this code in your paper, please cite our paper

@article{pham2018found,
  title={Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities},
  author={Pham, Hai and Liang, Paul Pu and Manzini, Thomas and Morency, Louis-Philippe and Poczos, Barnabas},
  journal={arXiv preprint arXiv:1812.07809},
  year={2018}
}
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