using DS3. For spectral clustering results, the extracted features are clustered into 101 clusters, and one sample from each cluster is selected randomly. Furthermore, for uncertainty-based selection, Bayesian active learning, as described in [@Gal2017DeepData; @Gal2016DropoutLearning], is utilized. For that, a dropout unit with parameter $0.2$ is added before the fully-connected layer and the uncertainty measure is computed by using $10$ forward iterations (following the implementation in [@Gal2016DropoutLearning]). In our experiments, we use variation ratio[^6] as the uncertainty metric, which is shown to be the most reliable metric among several well-known metrics [@Gal2017DeepData]. Also, for a fair comparison, the initial training set is the same for all the experiments.

It is evident that, during the first few cycles, since the classifier is not able to generate reliable uncertainty score, uncertainty-based selection does not lead to a performance gain. In fact, random selection outperforms uncertainty-based selection.

On the other hand, IPM is able to select the critical samples. In the first few active learning cycles, IPM is constantly outperforming other methods, which translates into significant reductions in labeling cost for applications such as video action recognition.

As the classifier is trained with more data, it is able to provide us with better 