Reviews: Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

This paper proposes a systematic evaluation of SSL methods, studies the pitfalls of current approaches to evaluation, and, conducts experiments to show the impact of rigorous validation on kinds of conclusions we can draw from these methods. I really like the paper and read it when it appeared on arXiv back in April. In many places we are lacking these kind of systematic approaches to robust evaluations and it's refreshing to see more of these papers emerging that question the foundation of our validation methodologies and provide a coherent evaluation. Suggestions for improvements: - The paper mainly deals with two image categorisation datasets. While these methods have been studied in many recent SSL papers, they also have their own limitations, some of which is mentioned in the paper. But the main problem is that it restricts them to a single domain which is image categorisation.