Curriculum Dropout

Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization. However, we show that using a fixed dropout probability during training is a suboptimal choice. We propose a time scheduling for the probability of retaining neurons in the network. This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem. This idea of "starting easy" and adaptively increasing the difficulty of the learning problem has its roots in curriculum learning and allows one to train better models. Indeed, we prove that our optimization strategy implements a very general curriculum scheme, by gradually adding noise to both the input and intermediate feature representations in the network architecture. The method, named Curriculum Dropout, yields to better generalization.

Code

Each sub-folder (...in progress...) is named after the dataset analyzed and equipped with its own README. The provided code runs with Python 2.7 (should run with Python 3 as well, not tested). For the installation of tensorflow-gpu please refer to the website.

The following command should install the main dependencies on most Linux (Ubuntu) machines

sudo apt-get install python-dev python-pip && sudo pip install -r requirements.txt

Download and extract MNIST

• The script download.sh downloads and extracts mnist. Deafult storing directory is ~/mnist.

sudo chmod a+x download.sh
./download.sh

Move the mnist/ folder wherever you like (e.g. /mydata) and then tell the training scripts where to find it

echo /mydata >> data_dir.txt

Reference

If you use this code as part of any published research, please acknowledge the following paper:

"Curriculum Dropout"
Pietro Morerio, Jacopo Cavazza, Riccardo Volpi, René Vidal and Vittorio Murino pdf

@InProceedings{Morerio2017dropout,
    title={Curriculum Dropout},
    author={Morerio, Pietro and Cavazza, Jacopo and Volpi, Riccardo and Vidal, Ren\'e and Murino, Vittorio},
    booktitle = {ICCV},
    year={2017}
} 

License

This repository is released under the GNU GENERAL PUBLIC LICENSE.