  Curriculum learning is a training strategy that sorts the training examples
by some measure of their difficulty and gradually exposes them to the learner
to improve the network performance. Motivated by our insights from implicit
curriculum ordering, we first introduce a simple curriculum learning strategy
that uses statistical measures such as standard deviation and entropy values to
score the difficulty of data points for real image classification tasks. We
empirically show its improvements in performance with convolutional and
fully-connected neural networks on multiple real image datasets. We also
propose and study the performance of a dynamic curriculum learning algorithm.
Our dynamic curriculum algorithm tries to reduce the distance between the
network weight and an optimal weight at any training step by greedily sampling
examples with gradients that are directed towards the optimal weight. Further,
we use our algorithms to discuss why curriculum learning is helpful.
