PyTorch implementation of "Regularizing Class-wise Predictions via Self-knowledge Distillation" (CVPR 2020).

torch==1.2.0, torchvision==0.4.0

train cifar100 on resnet with class-wise regularization losses

python3 train.py --sgpu 0 --lr 0.1 --epoch 200 --model CIFAR_ResNet18 --name test_cifar --decay 1e-4 --dataset cifar100 --dataroot ~/data/ -cls --lamda 1

train fine-grained dataset on resnet with class-wise regularization losses

python3 train.py --sgpu 0 --lr 0.1 --epoch 200 --model resnet18 --name test_cub200 --batch-size 32 --decay 1e-4 --dataset CUB200 --dataroot ~/data/ -cls --lamda 3

If you use this code for your research, please cite our papers.

@InProceedings{Yun_2020_CVPR,
author = {Yun, Sukmin and Park, Jongjin and Lee, Kimin and Shin, Jinwoo},
title = {Regularizing Class-Wise Predictions via Self-Knowledge Distillation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}