  With the rapid growth of data, it is becoming increasingly difficult to train
or improve deep learning models with the right subset of data. We show that
this problem can be effectively solved at an additional labeling cost by
targeted data subset selection(TSS) where a subset of unlabeled data points
similar to an auxiliary set are added to the training data. We do so by using a
rich class of Submodular Mutual Information (SMI) functions and demonstrate its
effectiveness for image classification on CIFAR-10 and MNIST datasets. Lastly,
we compare the performance of SMI functions for TSS with other state-of-the-art
methods for closely related problems like active learning. Using SMI functions,
we observe ~20-30% gain over the model's performance before re-training with
added targeted subset; ~12% more than other methods.
