Curriculum learning and Self paced learning are popular topics in the machine learning that suggest to put the training samples in order by considering their difficulty levels. Studies in these topics show that starting with a small training set and adding new samples according to difficulty levels improves the learning performance. In this paper we experimented that we can also obtain good results by adding the samples randomly without a meaningful order. We compared our method with classical training, Curriculum learning, Self paced learning and their reverse ordered versions. Results of the statistical tests show that the proposed method is better than classical method and similar with the others. These results point a new training regime that removes the process of difficulty level determination in Curriculum and Self paced learning and as successful as these methods.
This paper addresses an interesting problem of curriculum/self-paced versus random order of samples for faster learning. Specifically, the authors argue that adding samples in random order is as beneficial as adding them with some curriculum strategy, i.e. from easiest to hardest, or reverse. 
The main learning strategy considered in this work is learning with growing sets, i.e. at each next stage a new portion of samples is added to the current available training set. At the last stage, all training samples are considered. The classifier is re-learned on each stage, where optimized weights in the previous stage are given as initial weights in the next stage. 
The work has several flaws. 
-First of all, it is not surprising that learning with more training samples at each next stage (growing sets) gets better - this is the basic principle of learning. The question is how fast the current classifier converges to the optimal Bayes level when using Curriculum strategy versus Random strategy. The empirical evaluations do not show evidence/disprove regarding this matter. For example, it could happen that the classifier converges to the optimal on the first stage already, so there is no difference when training in random versus curriculum order with growing sets. 
-Secondly, easyness/hardness of the samples are defined w.r.t. some pre-trained (external) ensemble method. It is not clear how this definition of easiness/hardness translates when training the 3-layer neural network (final classifier). For example, it could well happen that all the samples are equally easy for training the final classifier, so the curriculum order would be the same as random order. In the original work on self-paced learning, Kumar et al (2010), easiness of the samples is re-computed on each stage of the classifier learning. 
-The empirical evaluations are not clear. Just showing the wins across datasets without actual performance is not convincing (Table 2). 
-I wonder whether the section with theoretical explanation is needed. What is the main advantage of learning with growing sets (when re-training the classifier)  and (traditional) learning when using the whole training dataset (last stage, in this work)?