Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning

Dependencies

• python3
• pytorch
• torchvision
• randAugment (Pytorch re-implementation: https://github.com/ildoonet/pytorch-randaugment)

Command for reproducing results in the paper

To train a model on CIFAR-10 with imbalanced ratio $\beta$ = 100, unlabeled ratio $\lambda$ = 2, random sampler for labeled data and random sampler for unlabeled data

python3 fix_train.py --gpu 0 --dataset cifar10 --imb_ratio 100 --ratio 2 \
--sampler random --semi-sampler random --out cifar10_fix_100_2_random_random

To fine-tune a model (here the model trained with above command) on CIFAR-10 with imbalanced ratio $\beta$ = 100, unlabeled ratio $\lambda$ = 2, mean sampler for labeled data and mean sampler for unlabeled data

python3 fix_finetune.py --gpu 0 --dataset cifar10 --imb_ratio 100 --ratio 2 \
--sampler mean --semi-sampler mean --resume cifar10_fix_100_2_random_random/checkpoint.pth.tar --out cifar10_fix_100_2_random_random_stage2

To train a Bi-Sampling model on CIFAR-10 with imbalanced ratio $\beta$ = 100, unlabeled ratio $\lambda$ = 2, random sampler + random sampler for the first stage and mean sampler + mean sampler for the second stage

python3 fix_BiS.py --gpu 0 --dataset cifar10 --imb_ratio 100 --ratio 2 \
--sampler1 random --semi-sampler1 random --sampler2 mean --semi-sampler2 mean --out cifar10_fix_100_2_BiS

To analyze the per-class precision and recall of a pertained model on CIFAR-10 with imbalanced ratio $\beta$ = 100, unlabeled ratio $\lambda$ = 2

python3 fix_analysis.py --gpu 0 --dataset cifar10 --imb_ratio 100 --ratio 2 \
--resume cifar10_fix_100_2_BiS/checkpoint.pth.tar