cost. Now, the fundamental question in active learning is: Given a fixed labeling budget, what are the best unlabeled data instances to be selected for labeling for the best performance?

In many active learning frameworks, new data points are selected based on the model uncertainty. However, the effect of such selection only kicks in after the size of the training set is large enough, so we can have a reliable uncertainty measure. In this section, we show that the proposed selection method can effectively find the best representatives of the data and outperforms several recent uncertainty-based and algebraic selection methods.

In particular, we study IPM for active learning of video action recognition, using the 3D ResNet18 architecture[^3], as described in [@Hara2017CanImageNet]. The experiments are run on UCF-101 human action dataset [@Soomro2012UCF101:Wild], and the network is pretrained on Kinetics-400 dataset [@Kay2017TheDataset]. Here, we provide the results on split 1. To ensure that at least one sample per class exists in the training set, for the initial training, one sample per class is selected randomly and the fully-connected layer of the classifier is fine tuned. Then, at each active learning cycle, one sample per class is selected, without the knowledge of the 