l_dmi's Introduction

Introduction

Implementation for NeurIPS 2019 paper :

paper link: https://arxiv.org/abs/1909.03388

Fashion MNIST dataset

To run experiments of Fashion MNIST dataset in fashion directory:

python3 fashion.py --r noisy_amount --s seed --c case_num --device device_num

noise_amount: the amount of noise amount r of label flipping. (0 <= r <= 1)
seed: random seed
case_num :  1: class-independent; 2: class-dependent (a); 3: class-dependent (b)
device_num: GPU number

CIFAR-10 dataset:

To run experiments of CIFAR10 dataset in CIFAR-10 directory, pleases run all the baseline in the following order:

python3 CE.py --r noise_amount --s seed --device device_num
python3 FW.py --r noise_amount --s seed --device device_num
python3 GCE.py --r noise_amount --s seed --device device_num
python3 LCCN.py --r noise_amount --s seed --device device_num
python3 DMI.py --r noise_amount --s seed --device device_num

noise_amount: the amount of noise amount r of label flipping. (0 <= r <= 1)
seed: random seed
device_num: GPU number

Dog & Cat datasete:

To run experiments of Dog vs. Cats dataset in dogcat directory, pleases run all the baseline in the following order:

python3 CE.py --r noise_amount --s seed --device device_num
python3 FW.py --r noise_amount --s seed --device device_num
python3 GCE.py --r noise_amount --s seed --device device_num
python3 LCCN.py --r noise_amount --s seed --device device_num
python3 DMI.py --r noise_amount --s seed --device device_num

noise_amount: the amount of noise amount r of label flipping. (0 <= r <= 1)
seed: random seed
device_num: GPU number

Clothing1M dataset

To run experiments of Clothing1M dataset in clothing directory, pleases run all the baseline in the following order:
```
python3 main.py --device device_num

device_num: GPU number
```