Review for NeurIPS paper: Big Self-Supervised Models are Strong Semi-Supervised Learners

Weaknesses: - Most major parts in this work, such as distillation, fine-tuning are proposed in previous works. Although the authors improve SimCLR and proposed SimCLR V2, the novelties in individual parts are somehow limited. However, I think the simple semi-supervised framework is still valuable for industry and future works. It explicitly points out a previously ignored paradigm in semi-supervised visual learning where regularization based methods dominates. I think it will inspire several future works following the paradigm.