https://github.com/Gordonjo/versa

Code to reproduce experiments in "Meta-learning probabilistic inference for prediction"
https://github.com/Gordonjo/versa

Last synced: 13 days ago
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Code to reproduce experiments in "Meta-learning probabilistic inference for prediction"

• Host: GitHub
• URL: https://github.com/Gordonjo/versa
• Owner: Gordonjo
• License: mit
• Created: 2018-06-14T12:00:34.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-02-19T12:16:51.000Z (over 3 years ago)
• Last Synced: 2024-10-26T18:55:27.633Z (13 days ago)
• Language: Python
• Homepage:
• Size: 39.1 KB
• Stars: 67
• Watchers: 7
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
Meta-Learning Probabilistic Inference For Prediction

====================================================

This repository implements the models and algorithms necessary to reproduce the experiments carried out in the paper

`Meta-Learning Probabilistic Inference For Prediction, Gordon and Bronskill et al. `_

It includes code for running few-shot classification experiments with Omniglot and miniImageNet, as well as for reproducing

the ShapeNet view reconstruction experiments.

The code has been authored by: John Bronskill, Jonathan Gordon, and Matthias Bauer.

The main components of the repository are:

* ``run_classifier.py``: script to run classification experiments on Omniglot and miniImageNet

* ``train_view_reconstruction.py``: script to train view recovery models using ShapeNet objects

* ``evaluate_view_reconstruction.py``: script to test view recovery models using ShapeNet objects

* ``features.py``: deep neural networks for feature extraction and image generation

* ``inference.py``: amortized inference networks for various versions of Versa

* ``utilities.py``: assorted functions to support the repository

Dependencies

------------

This code requires the following:

* python 2 or python 3

* TensorFlow v1.0+

Data

----

For Omniglot, miniImagenet, and ShapeNet data, see the usage instructions in ``data/save_omniglot_data.py``, ``data/save_mini_imagenet_data.py``, and ``data/save_shapenet_data.py``, respectively.

Note that users of the ShapeNet data must abide by the `ShapeNet Terms of Use `_.

Usage

-----

* To run few-shot classification, see the usage instructions at the top of ``run_classifier.py``.

* To run view reconstruction, see the usage instructions at the top of ``train_view_reconstruction.py`` and  ``evaluate_view_reconstruction.py``.

Contact

-------

To ask questions or report issues, please open an issue on the issues tracker.

Extending the Model

-------------------

There are a number of ways the repository can be extended:

* **Data**: to use alternative datasets, a class must be implemented to handle the new dataset. The necessary methods for the class are: ``__init__``, ``get_batch``, ``get_image_height``, ``get_image_width``, and ``get_image_channels``. For example signatures see ``omniglot.py``, ``mini_imagenet.py`` or ``omniglot.py``. Note that the code currently handles only image data. Finally, add the initialization of the class to the file ``data.py``.

* **Feature extractors**: to use alternative feature extractors, simply implement a desired feature extractor in ``features.py`` and change the function call in ``run_classifier.py``. For the required signature of a feature extractor see the function ``extract_features`` in ``features.py``.

Citation

--------

If you use this code for your research, please cite our `paper `_:

::

  @inproceedings{gordon2018metalearning,

    title={Meta-Learning Probabilistic Inference for Prediction},

    author={Jonathan Gordon and John Bronskill and Matthias Bauer and Sebastian Nowozin and Richard Turner},

    booktitle={International Conference on Learning Representations},

    year={2019},

    url={https://openreview.net/forum?id=HkxStoC5F7},

  }