CoLafier

This repository is the official implementation of the SDM2024 paper "CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance". Some code scripts are adapted from DISC.

Requirements

Language

• Python3 == 3.9.12

Modules

• wandb==0.10.11
• torch==2.0.0+cu118
• addict==2.4.0
• torchvision==0.15.1+cu118
• tqdm==4.64.1
• scikit-learn==1.2.0
• matplotlib==3.6.2
• numpy==1.24.2
• Pillow==9.4.0

These packages can be installed directly by running the following command:

pip install -r requirements.txt

Datasets

CIFAR-10

CIFAR-10 dataset can be downloaded from link.

CIFAR-10N

CIFAR-10N labels are provided at cifar-10-100n. In our paper, we used the "Aggregate", "Random1", and "Worse" labels.

If you want to use one of the datasets, please download it into your data directory and change the data path in bash scripts (see the following section).

Training

CIFAR-10 with symmetric or asymmetric noise

noise_type=${1:-'sym'} # 'sym' or 'asym' 
gpuid=${2:-'0'}
seed=${3:-'1'}
save_path=${4:-'./logs/'}
data_path=${5:-'../data'} # path to the CIFAR-10 data
config_path=${6:-'./configs/colafier_sym_asym.py'}
dataset=${7:-'cifar-10'}
num_classes=${8:-10}
noise_rate=${9:-0.4}
performance_path=${10:-'test_performance'}

python main.py
  -c=$config_path
  --save_path=$save_path
  --noise_type=$noise_type
  --seed=$seed --gpu=$gpuid
  --percent=$noise_rate
  --dataset=$dataset
  --num_classes=$num_classes
  --root=$data_path
  --performance_path=$performance_path
  --noise_path=$noise_path

CIFAR-10 with instance-dependent noise or CIFAR-10N

noise_type=${1:-'ins'} # 'ins' or 'aggre_label' or 'worse_label' or 'random_label1' 
gpuid=${2:-'0'}
seed=${3:-'1'}
save_path=${4:-'./logs/'}
data_path=${5:-'../data'} # path to the CIFAR-10/CIFAR-10N data
config_path=${6:-'./configs/colafier_ins_real.py'}
dataset=${7:-'cifar-10'} 
num_classes=${8:-10}
noise_rate=${9:-0.4} # set 0.0 for CIFAR-10N
performance_path=${10:-'test_performance'}

python main.py
  -c=$config_path
  --save_path=$save_path
  --noise_type=$noise_type
  --seed=$seed --gpu=$gpuid
  --percent=$noise_rate
  --dataset=$dataset
  --num_classes=$num_classes
  --root=$data_path
  --performance_path=$performance_path
  --noise_path=$noise_path