text: 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.