A deep reinforcement active learning method for multi-label image classification

Active learning is a widely used method for addressing the high cost of sample labeling in deep learning models and has achieved significant success in recent years. However, most existing active learning methods only focus on single-label image classification and have limited application in the context of multi-label images. To address this issue, we propose a novel, multi-label active learning approach based on a reinforcement learning strategy. The proposed approach introduces a reinforcement active learning framework that accounts for the expected error reduction in multi-label images, making it adaptable to multi-label classification models. Additionally, we develop a multi-label reinforcement active learning module (MLRAL), which employs an actor-critic strategy and proximal policy optimization algorithm (PPO). Our state and reward functions consider multi-label correlations to accurately evaluate the potential impact of unlabeled samples on the current model state. We conduct experiments on various multi-label image classification tasks, including the VOC 2007, MS-COCO, NUS-WIDE and ODIR. We also compare our method with multiple classification models, and experimental results show that our method outperforms existing approaches on various tasks, demonstrating the superiority and effectiveness of the proposed method.