Multi-criteria active deep learning for image classification

As a robust and heuristic technique in machine learning, active learning has been established as an effective method for addressing large volumes of unlabeled data; it interactively queries users (or certain information sources) to obtain desired outputs at new data points. With regard to deep learning techniques (e.g., CNN) and their applications (e.g., image classification), labeling work is of great significance as training processes for obtaining parameters in neural networks which requires abundant labeled samples. Although a few active learning algorithms have been proposed for devising certain straightforward sampling strategies (e.g., density, similarity, uncertainty, and label-based measure) for deep learning algorithms, these employ onefold sampling strategies and do not consider the relationship among multiple sampling strategies. To this end, we devised a novel solution “multi-criteria active leep learning”(MCADL) to learn an active learning strategy for deep neural networks in image classification. Our sample selection strategy selects informative samples by considering multiple criteria simultaneously (i.e., density, similarity, uncertainty, and label-based measure). Moreover, our approach is capable of adjusting weights adaptively to fuse the results from multiple criteria effectively by exploring the utilities of the criteria at different training stages. Through extensive experiments on two popular image datasets (i.e., MNIST and CIFAR-10), we demonstrate that our proposed method consistently outperforms highly competitive active learning approaches; thereby, it can be verified that our multi-criteria active learning proposal is rational and our solution is effective.