State-Relabeling Adversarial Active Learning

Code for SRAAL [2020 CVPR Oral]

Requirements

torch >= 1.6.0

numpy >= 1.19.1

tqdm >= 4.31.1

AL Results

The AL sampling starts from 10% initial labeled pool(10.npy) and selects 5% data to label at each iteration.

The result files locate in ./results_cifar100/

To Train the Model

python main.py

To Evaluate the Results

python acc100.py