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An Analysis of Unsupervised Pre-training in Light of Recent Advances

This repo contains the experiment files for the paper "An Analysis of Unsupervised Pre-training in Light of Recent Advances", available here: http://arxiv.org/abs/1412.6597

Abstract

Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets. Unsupervised data has been proposed as another way to improve performance. Unfortunately, unsupervised pre-training is not used by state-of-the-art methods leading to the following question:
Is unsupervised pre-training still useful given recent advances? If so, when?
We answer this in three parts: we

1. develop a unsupervised method that incorporates ReLUs and recent unsupervised regularization techniques
2. analyze the benefits of unsupervised pre-training compared to data augmentation and dropout on CIFAR-10 while varying the ratio of unsupervised to supervised samples
3. verify our findings on STL-10.

We discover unsupervised pre-training, as expected, helps when the ratio of unsupervised to supervised samples is high, and surprisingly, hurts when the ratio is low.

We also use unsupervised pre-training with additional color augmentation to achieve near state-of-the-art performance on STL-10.

Bibtex

@article{paine2014analysis,
  title={An Analysis of Unsupervised Pre-training in Light of Recent Advances},
  author={Paine, Tom Le and Khorrami, Pooya and Han, Wei and Huang, Thomas S},
  journal={arXiv preprint arXiv:1412.6597},
  year={2014}
}

About the repo

The experiments are split into two sections:

• cifar10
• stl10

The README.md file in each folder will give you more information about running experiments.

The experiments are written in python 2.7, and require open source software to run, including:

• numpy, a standard numerical computing library for python.
• anna, our neural network library, which itself depends on theano and pylearn2. The pylearn dependencies are relatively small, and we may remove them to limit the number of dependencies.

Installation Note

After successfully installing pylearn2 and anna, the user needs to follow the three steps below before training the convolutional autoencoders:

1. Go to the pylearn2 root directory.
2. Open the ./pylearn2/sandbox/cuda_convnet/pool.py file.
3. Add the following function to the MaxPoolGrad class.

def grad(self, inp, grads):
        """
        .. todo::

            WRITEME
        """
        a, b, c = inp
        ga = gpu_contiguous(a*0)
        gb = gpu_contiguous(b*0)
        gc = gpu_contiguous(c)
        gz, = grads
        gz = gpu_contiguous(gz)
        return [ga, gb, MaxPoolRop(self.ds, self.stride)(a, gz)]

Status

All experiments are added. Just need to finalize documentation.