Reviews: MixMatch: A Holistic Approach to Semi-Supervised Learning

Originality: 7 Quality:8 Clarity: 4 Significance:7 Mixmatch combined a lot of classical extraordinary methods that used for semi-supervised learning and achieved state-of-the-art results by a large margin across many datasets and labeled data amounts. Compared to previous method, this method is not only a simple combination of different data augmentation methods and other methods, such as exponential model average (EMA), it also explores a path to fully combine the advantages of different methods. In short, this method is of course a big step for semi-supervised learning on image classification. However, the experiments on this paper still needs to be modified to be perfect and a fair comparison with previous paper, such as Mean-Teacher. Also, some small problems need to be fixed to be finally published.