Deep similarity-based batch mode active learning with exploration-exploitation

Active learning aims to reduce manual labeling efforts by proactively selecting the most informative unlabeled instances to query. In real-world scenarios, it's often more practical to query a batch of instances rather than a single one at each iteration. To achieve this we need to keep not only the informativeness of the instances but also their diversity. Many heuristic methods have been proposed to tackle batch mode active learning problems, however, they suffer from two limitations which if addressed would significantly improve the query strategy. Firstly, the similarity amongst instances is simply calculated using the feature vectors rather than being jointly learned with the classification model. This weakens the accuracy of the diversity measurement. Secondly, these methods usually exploit the decision boundary by querying the data points close to it. However, this can be inefficient when the labeled set is too small to reveal the true boundary. In this paper, we address both limitations by proposing a deep neural network based algorithm. In the training phase, a pairwise deep network is not only trained to perform classification, but also to project data points into another space, where the similarity can be more precisely measured. In the query selection phase, the learner selects a set of instances that are maximally uncertain and minimally redundant (exploitation), as well as are most diverse from the labeled instances (exploration). We evaluate the effectiveness of the proposed method on a variety of classification tasks: MNIST classification, opinion polarity detection, and heart failure prediction. Our method outperforms the baselines with both higher classification accuracy and faster convergence rate.