Active learning in multi-label image classification with graph convolutional network embedding

Active learning has achieved considerable success in sample selection for deep learning models and has been widely used to address the issue of high-cost sample annotation. However, most of the existing active learning methods focus on single-label image classification and have limited use in multi-label scenarios. To address this issue and take advantage of label associations, we propose an active learning model based on the graph convolutional network (GCN) embedding and loss prediction network. Specifically, we construct a heterogeneous information network (HIN) that uses GCN embeddings to learn multiple label associations, as well as associations between images and labels. We also use a loss prediction network to predict target losses of unlabeled inputs. In addition, we propose a dynamic active coefficient to adjust the proportion of active learning gradually in the training process. Comprehensive multi-label image classification experiments with limited training labels are conducted on the MS-COCO, VOC 2007, and NUS-WIDE datasets. The comparison results demonstrate the superiority of our method compared with conventional methods in terms of both classification accuracy and convergence speed.