Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem. However, choosing a non-trivial scheduling method may drastically improve convergence. In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP). Our method parameterizes mini-batches dynamically based on the easiness and true diverseness of the sample within a salient feature representation space. In LEAP, we train an embedding Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss. The student CNN classifier dynamically selects samples to form a minibatch based on the easiness from cross-entropy losses and true diverseness of examples from the representation space sculpted by the embedding CNN. We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN. We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.
The authors purpose a method for creating mini batches for a student network by using a second learned representation space to dynamically selecting  examples by their 'easiness and true diverseness'. The framework is detailed and results on MNIST, cifar10 and fashion-MNIST are presented. The work presented is novel but there are some notable omissions: 
 - there are no specific numbers presented to back up the improvement claims; graphs are presented but not specific numeric results
- there is limited discussion of the computational cost of the framework presented 
- there is no comparison to a baseline in which the additional learning cycles used for learning the embedding are used for training the student model.
- only small data sets are evaluated. This is unfortunate because if there are to be large gains from this approach, it seems that they are more likely to be found in the domain of large scale problems, than toy data sets like mnist. 
**edit
In light of the changes made, and in particular the performance gains achieved on CIFAR-100, i have increased my ratting from a 4 to a 6