Multi-Sample Dropout PyTorch
This repository contains a PyTorch implementation of the
Multi-Sample Dropout from the paper
by Hiroshi Inoue.
Multi-Sample Dropout is a new way to expand the traditional Dropout by using multiple dropout masks for the same mini-batch.
The code in this repository implements Multi-Sample Dropout training, with example on the CIFAR-10 datasets.
To use Multi-Sample Dropout use the following command.
python run.py --dropout_num = 8
To experiment th result,we use CIFAR-10 dataset for MiniResNet.
# no dropout python run.py --dropout_num=0 # sample = 1 python run.py --dropout_num=1 # sample = 8 python run.py --dropout_num=8
Train loss of Multi-Sample Dropout with MiniResNet on CIFAR-10.
Valid loss of Multi-Sample Dropout with MiniResNet on CIFAR-10.
Valid accuracy of Multi-Sample Dropout with MiniResNet on CIFAR-10.