labels, and added to the training set. Next, using the updated training set, the fully connected layer of the network is fine tuned for $60$ epochs, using learning rate of $10^{-1}$, weight decay of $10^{-3}$, and batch size of $24$ on 2 GPUs. Rest of the implementation and training settings are the same as [@Hara2017CanImageNet]. Note that, in this experiment, fine-tuning is only performed to train the fully connected layer, because it achieved the best accuracy during the preliminary investigation for very small training sets, which is the scope of this experiment. Using this setting the classification accuracy using the full training set is $82.23\%$ [^4].

The selection is performed on the convolutional features extracted from the last convolutional layer of the network. Table \[tab:AL\_results\] shows the accuracy of the trained network at each active learning cycle for different selection methods. In the table, DS3 stands for dissimilarity-based sparse subset selection, which has been proposed for finding an informative subset of a collection of data points[^5] [@Elhamifar2016DissimilaritySelection]. The high computational complexity of DS3 prevents its implementation on all the data. So, we provide the results for DS3 only for the clustered version, meaning that one sample per cluster is selected 