https://github.com/Warvito/discovering-hidden-factors-of-variation-in-deep-networks

Tensorflow 2.0 implementation of "Discovering hidden factors of variation in deep networks"
https://github.com/Warvito/discovering-hidden-factors-of-variation-in-deep-networks

autoencoder deep-learning mnist-dataset tensorflow tensorflow-2

Last synced: 9 days ago
JSON representation

Tensorflow 2.0 implementation of "Discovering hidden factors of variation in deep networks"

• Host: GitHub
• URL: https://github.com/Warvito/discovering-hidden-factors-of-variation-in-deep-networks
• Owner: Warvito
• License: mit
• Created: 2019-04-11T16:00:20.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-04-11T16:07:15.000Z (over 5 years ago)
• Last Synced: 2024-10-06T17:42:55.571Z (30 days ago)
• Topics: autoencoder, deep-learning, mnist-dataset, tensorflow, tensorflow-2
• Language: Jupyter Notebook
• Size: 732 KB
• Stars: 2
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-tensorflow-2 - Discovering hidden factors of variation in deep networks

README

        
# Discovering hidden factors of variation in deep networks

[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](https://github.com/Warvito/discovering-hidden-factors-of-variation-in-deep-networks/blob/master/LICENSE)

 This is a Tensorflow 2.0 implementation of [Discovering hidden factors of variation in deep networks](https://arxiv.org/abs/1412.6583) by [Brian Cheung](https://twitter.com/thisismyhat) et al. (2014). This repository contains reproduce of several experiments mentioned in the paper. Based on the Lasagne (RIP) example ([link](https://github.com/Lasagne/Lasagne/blob/highway_example/examples/Hidden%20factors.ipynb)).

 

## Abstract

Deep learning has enjoyed a great deal of success because of its ability to learnuseful  features  for  tasks  such  as  classification.   But  there  has  been  less  explo-ration in learning the factors of variation apart from the classification signal.  Byaugmenting  autoencoders  with  simple  regularization  terms  during  training,  wedemonstrate  that  standard  deep  architectures  can  discover  and  explicitly  repre-sent factors of variation beyond those relevant for categorization.  We introducea cross-covariance penalty (XCov) as a method to disentangle factors like hand-writing style for digits and subject identity in faces.  We demonstrate this on theMNIST handwritten digit database,  the Toronto Faces Database (TFD) and theMulti-PIE dataset by generating manipulated instances of the data.  Furthermore,we demonstrate these deep networks can extrapolate ‘hidden’ variation in the supervised signal.

 

## Requirements

- Python 3

- [TensorFlow 2.0+](https://www.tensorflow.org/)

- [Numpy](http://www.numpy.org/)

- [Matplotlib](https://matplotlib.org/)

## Installing the dependencies

Install virtualenv and creating a new virtual environment:

    pip install virtualenv

    virtualenv -p /usr/bin/python3 ./venv

Install dependencies

    pip3 install -r requirements.txt

## Disclaimer

This is not an official implementation.

## Citation

If you find this code useful for your research, please cite:

    @article{cheung2014discovering,

      title={Discovering hidden factors of variation in deep networks},

      author={Cheung, Brian and Livezey, Jesse A and Bansal, Arjun K and Olshausen, Bruno A},

      journal={arXiv preprint arXiv:1412.6583},

      year={2014}

    }