Reproducing the result of cifar100

[Ldoun]

Hello, first and foremost, I would like to express my gratitude for the work you have done. Thank you. I was trying to reproduce the result of cifar100 in the appendix using retrain and FT. But for some reason, I couldn't reproduce the result of unlearning by FT.

I tested cifar100 under 6 different independent trials. As you can see FT with class-wise forgetting has quite a low score of forget efficacy on Sparse(pruned model)/Dense(unpruned) class-wise forgetting and Sparse random data forgetting.

The hyperparameters of these experiments are the same as ciafar10.

for seed in 1 2 3 4 5 6
do
    python -u main_forget.py --save_dir ${save_dir}/random/${seed}_retrain --mask ${base_dir}/${seed}/base/0model_SA_best.pth.tar --unlearn retrain --unlearn_epochs 160 --unlearn_lr 0.1 --dataset $data --class_to_replace -1 --num_indexes_to_replace 4500 --seed $seed
    python -u main_forget.py --save_dir ${save_dir}/random/${seed}_FT_only --mask ${base_dir}/${seed}/base/0model_SA_best.pth.tar --unlearn FT --unlearn_lr 0.04 --unlearn_epochs 10 --dataset $data --class_to_replace -1 --num_indexes_to_replace 4500 --seed $seed
    
    python -u main_forget.py --save_dir ${save_dir}/class/${seed}_retrain --mask ${base_dir}/${seed}/base/0model_SA_best.pth.tar --unlearn retrain --unlearn_epochs 160 --unlearn_lr 0.1 --dataset $data --seed $seed
    python -u main_forget.py --save_dir ${save_dir}/class/${seed}_FT_only --mask ${base_dir}/${seed}/base/0model_SA_best.pth.tar --unlearn FT --unlearn_lr 0.01 --unlearn_epochs 10 --dataset $data --seed $seed

    python -u main_forget.py --save_dir ${save_dir2}/random/${seed}_retrain --mask ${base_dir}/${seed}/base/1model_SA_best.pth.tar --unlearn retrain --unlearn_epochs 160 --unlearn_lr 0.1 --dataset $data --class_to_replace -1 --num_indexes_to_replace 4500 --seed $seed
    python -u main_forget.py --save_dir ${save_dir2}/random/${seed}_FT_only --mask ${base_dir}/${seed}/base/1model_SA_best.pth.tar --unlearn FT --unlearn_lr 0.04 --unlearn_epochs 10 --dataset $data --class_to_replace -1 --num_indexes_to_replace 4500 --seed $seed
    
    python -u main_forget.py --save_dir ${save_dir2}/class/${seed}_retrain --mask ${base_dir}/${seed}/base/1model_SA_best.pth.tar --unlearn retrain --unlearn_epochs 160 --unlearn_lr 0.1 --dataset $data --seed $seed
    python -u main_forget.py --save_dir ${save_dir2}/class/${seed}_FT_only --mask ${base_dir}/${seed}/base/1model_SA_best.pth.tar --unlearn FT --unlearn_lr 0.01 --unlearn_epochs 10 --dataset $data --seed $seed
done

#4 I'm aware that you've used different learning rates of cifar10 for class-wise forgetting(0.01) and random data forgetting(0.04). Is learning rates for cifar100 different?

Thank you.