Multi-Label Deep Active Learning with Label Correlation

Annotating a data sample in a multi-label learning problem requires a human oracle to consider the presence/absence of every possible label separately, which is extremely labor intensive. Active learning algorithms automatically identify the informative samples from large amounts of unlabeled data and significantly reduce human annotation efforts in inducing a classification model. Further, deep models have gained popularity to automatically learn representative features from a given dataset and have depicted promising empirical performance in a variety of applications. In this paper, we exploit the feature learning capabilities of deep neural networks and propose a novel framework to address the problem of multi-label active learning with label correlation. We integrate an active selection criterion to the objective function and train deep networks to optimize the function. Our extensive empirical studies on five benchmark multi-label datasets show that our methods outperform the state-of-the-art algorithms, corroborating their potential for real-world image classification applications.