Independency-enhancing adversarial active learning

The core idea of active learning is to obtain higher model performance with less annotation cost. This paper proposes an independency-enhancing adversarial active learning method. Independency-enhancing adversarial active learning is different from the previous methods and pays more attention to sample independence. Specifically, it is believed that the informativeness of a group of samples is related to sample independence rather than the simple sum of the informativeness of each sample in the group. Therefore, an independent sample selection module based on hierarchical clustering is designed to ensure sample independence. An adversarial approach is used to learn the feature representation of a sample and use the predicted loss value to label the state of the sample. Finally, samples are selected according to the uncertainty of the samples, the diversity of the samples and the independence of the samples. The experimental results on four datasets (CIFAR-100, Caltech-101, Cityscapes and BDD100K) demonstrate the effectiveness and superiority of independency-enhancing adversarial active learning.