  Deep learning has yielded extraordinary results in vision and natural
language processing, but this achievement comes at a cost. Most deep learning
models require enormous resources during training, both in terms of computation
and in human labeling effort. In this paper, we show that one can achieve
similar accuracy to traditional deep-learning models, while using less training
data. Much of the previous work in this area relies on using uncertainty or
some form of diversity to select subsets of a larger training set.
Submodularity, a discrete analogue of convexity, has been exploited to model
diversity in various settings including data subset selection. In contrast to
prior methods, we propose a novel diversity driven objective function, and
balancing constraints on class labels and decision boundaries using matroids.
This allows us to use efficient greedy algorithms with approximation guarantees
for subset selection. We outperform baselines on standard image classification
datasets such as CIFAR-10, CIFAR-100, and ImageNet. In addition, we also show
that the proposed balancing constraints can play a key role in boosting the
performance in long-tailed datasets such as CIFAR-100-LT.
