[Official] ALASCA: Adative Label Smoothing via Auxiliary Classifier

This repository contains code for AAAI-23 Paper "A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise"

How to use

All steps start from the root directory.

0. Set environment setup

pip install -r requirements.txt

1. ALASCA (CIFAR Symmetric 50% Noise)

python train_cifar.py --gpu 0 --r 0.5 --noise-type sym --epochs 120 --dataset cifar10 --loss-fn ce --wd 1e-3 --out ./saved_models/ --alasca --position_all

2. ALASCA + SCE (CIFAR Asymmetric 40% Noise)

python train_cifar.py --gpu 0 --r 0.4 --noise-type asym --epochs 120 --dataset cifar10 --loss-fn sce --wd 1e-3 --out ./saved_models/ --alasca --position_all

3. ALASCA + Co-teaching (CIFAR Instance-dependent 40% Noise)

python train_cifar.py --gpu 0 --r 0.4 --noise-type sym --epochs 120 --dataset cifar10 --loss-fn ce --num_gradual 30 --use_multi_networks --multi_networks_method coteach --out ./saved_models/ --alasca --position_all

References

• Coteaching (https://github.com/bhanML/Co-teaching)
• ELR (https://github.com/shengliu66/ELR)
• Active-Passive-Losses (https://github.com/HanxunH/Active-Passive-Losses)