DEEP ADVERSARIAL ACTIVE LEARNING WITH MODEL UNCERTAINTY FOR IMAGE CLASSIFICATION

Active learning aims at selecting and labeling as few samples as possible to train a good task model. Most existing methods rely on various heuristics to iteratively select a single sample in each active learning loop, thus cannot tackle large datasets efficiently. In this paper, we propose a new batch-mode active learning method, which can plug model prediction uncertainty into adversarial batch selection to ensure the selected samples are representative in unlabeled data, complementary to labeled data, and beneficial for model training. Experiments on four benchmark image datasets validate the effectiveness and efficiency of the proposed method for active image classification in comparison with the state-of-the-art methods.