Since the creation of Generative Adversarial Networks (GANs), much work has been done to improve their training stability, their generated image quality, their range of application but nearly none of them explored their self-training potential. Self-training has been used before the advent of deep learning in order to allow training on limited labelled training data and has shown impressive results in semi-supervised learning. In this work, we combine these two ideas and make GANs self-trainable for semi-supervised learning tasks by exploiting their infinite data generation potential. Results show that using even the simplest form of self-training yields an improvement. We also show results for a more complex self-training scheme that performs at least as well as the basic self-training scheme but with significantly less data augmentation.
The paper presents to combine self-learning and GAN. The basic idea is to first use GAN to generate data, and then infer the pseudo label, and finally use the pseudo labeled data to enhance the learning process. Experiments are conducted on one image data set. The paper contains several deficiencies.
1.	The experiment is weak. Firstly, only one data set is employed for evaluation, which is hard to justify the applicability of the proposed approach. Secondly, the compared methods are too few and do not include many state-of-the-art SSL methods like graph-based approaches. Thirdly, in these cases, the results in table 1 contain evident redundancy. Fourthly, the performance improvement over compared method is not significant and the result is based on 3 splits of data set, which is obviously not convincing and involves large variance. 
2.	The paper claims that ‘when paired with deep, semi-supervised learning has had a few success’. I do not agree with such a claim. There are many success SSL deep learning studies on embedding. They are not included in the discussions. 
3.	The layout of the paper could be improved. For example, there are too many empty spaces in the paper. 
4.	Overall technically the proposed approach is a bit straightforward and does not bring too much novelty.
5.	The format of references is not consistent. For example, some conference has short name, while some does not have.