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Enhanced Meta Label Correction (EMLC)

Description ℹ️

This is an official PyTorch implementation of the "Enhanced Meta Label Correction for Coping with Label Corruption" paper.

Results 📈

CIFAR-10 with 1k clean samples:

Method/Noise 20% 50% 80% 90% Asym. 40%
MLC 92.6 88.1 77.4 67.9 –
Previous SOTA 93.4 90.07 86.78 79.52 91.6
EMLC 93.53 92.63 90.95 90.71 91.82

CIFAR-100 with 1k clean samples:

Method/Noise 20% 50% 80% 90%
MLC 66.5 52.4 18.9 14.2
Previous SOTA 72.5 65.4 55.22 16.7
EMLC 73.05 68.61 60.51 54.04

Clothing1M:

Method Accuracy
MLC 75.78
Previous SOTA 77.83
EMLC 79.35

Running The Code 🏃

Before training the models please:

  1. Put the datasets in the data sub-directory.
  2. For the CIFAR experiments, put the self-supervised pretrained models in the pretrained sub-directory. You may use SupContrast implementation for training the SSL models.
  3. For the Clothing experiment, run the preprocessing procedure:
python CLOTHING1M/load_cloth1m_data.py --root=[path_to_clothing_dataset]
  1. Go over the dependencies.

Training 🏋️

To start training on the Clothing1M dataset run:

python main_clothing.py --gpuid=a --n_gpus=b

To start training on the CIFAR datasets run:

python main_cifar.py --gpuid=a --n_gpus=b --dataset=c --corruption_type=d --corruption_level=e --ssl_path=f

The parameters and their meaning:

Parameter Description Type Possible values
gpuid The id of the primary GPU to train on int [0,#GPUS)
n_gpus Number of GPUs to train on int [1,#GPUS]
dataset CIFAR dataset to train on string Either cifar10 or cifar100
corruption_type Type of artifical noise string unif for symmetric noise injection and flip for asymmetric noise injection
corruption_level Injected Noise level float (0,1)
ssl_path Path to the relevant SSL pretrained model string pretrained/*.pth

Dependencies 💾

Before trying to run anything please ensure that the packages below are up to date.

Library Minimal Version
NumPy 1.23.5
PyTorch 1.13.1
torchvision 0.14.1
tensorboard 2.11.2
argparse 1.1
tqdm 4.64.0

Credits ©️

This repository is heavily based on MLC.