This repository contains a reference implementation (in PyTorch) for "E. Littwin, L. Wolf. Regularizing by the Variance of the Activations' Sample-Variances. Neural Information Processing Systems (NIPS), 2018."

1. Introduction
2. Usage

1. Download and extract CIFAR 10 for python in your data dir
2. Clone this repository
3. Train using the instructions given in Train section

• Python2.7
• PyTorch(0.4.0+)

As an example, use the following command to train a network with 11 convolution layers and the variance constancy loss:

python vcl_tests_main.py --bn 0 --use_reg 1 --out_file elu11 --device 0 --model elu11

For changing the data directory, please add:

--train_path_10 <train batches directory for CIFAR10> --test_path_10 <test batch directory for CIFAR10>

A CSV file with the best and latest models will be saved to the checkpoint directory

For detailed options, please python vcl_tests_main.py --help

This code is a simplified implementation in pytorch for a code written by Etai Littwin