Learning Succinct Common Representation with Wyner's Common Information

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This repository provides a codebase for the paper [https://arxiv.org/abs/1905.10945]("Learning Succinct Common Representation with Wyner's Common Information," (2022)).

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Experiments

###1. MNIST Add-1 / MNIST--SVHN experiment

Prepare datasets

• Download this zip file (mnist-add1-svhn.zip) and put the files under data/mnist-add1 and data/mnist-svhn.
• For evaluation, download this zip file (autoencoders.zip) and put the files under pretrained/autoencoders.

Usage

To be updated.

###2. CUB Image--Caption experiment

Prepare datasets

We used the Caltech--UCSD Birds (CUB) dataset based on this repository (MMVAE).

• Follow the procedure as described in https://github.com/iffsid/mmvae#cub-image-caption.
• Please restructure the folder as follows:

data/cub
│───img
│   │───train
│   └───test
└───sent
    │───text_testclasses.txt
    └───text_testclasses.txt

Usage

To be updated.

###3. Zero-shot sketch retrieval experiment

Prepare datasets

• Download the Sketchy Extended dataset by following this repository (SEM-PCYC).
• Put the dataset under data/Sketchy which should look like:

data/Sketchy
│───extended_photo
│    │───airplane
│    │    └───...jpg
│    │───alarm
│    │    └───...jpg
│    │───...
│    └───zebra
│         └───...jpg
│───photo
│   └───tx_000000000000
│        │───airplane
│        │    └───...jpg
│        │───alarm
│        │    └───...jpg
│        │───...
│        └───zebra
│             └───...jpg
│───pretrained
│   │───vgg16_photo.pth
│   └───vgg16_sketch.pth
│───sketch
│   └───tx_000000000000
│        │───airplane
│        │    └───...png
│        │───alarm
│        │    └───...png
│        │───...
│        └───zebra
│             └───...png
│───README.txt
└───test_split_eccv2018.txt

Usage

To be updated.

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Credits

We have written our code largely building upon the following existing code bases:

• An overall codebase design, MNIST--SVHN dataset, and CUB Image-Caption experiment: https://github.com/iffsid/mmvae
• Frechet distance: https://github.com/mseitzer/pytorch-fid
• Zero-shot sketch retrieval experiment
  • Dataset preparation: https://github.com/AnjanDutta/sem-pcyc
  • Evaluation: https://github.com/gr8joo/IIAE/