Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe;  training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a  very large collection (i.e. active learning).

 Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs when applied in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, i.e. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.

We approach to the problem of active learning as a core-set selection problem and show that this approach is especially useful in the batch active learning setting which is crucial when training CNNs. The authors provide an algorithm-agnostic active learning algorithm for multi-class classification The paper proposes a batch mode active learning algorithm for CNN as a core-set problem which outperforms random sampling and uncertainty sampling. Studies active learning for convolutional neural networks and formulates the active learning problem as core-set selection and presents a novel strategy