reviews: 
Comments: The paper proposes to apply contrastive learning for an interesting task setting, open-world semi-supervised learning. Though there has been previous work that attempts to introduce contrastive learning for semi-supervised learning (i.e., OpenCon), the paper claims that previous work does not consider the open-world task setting, which leads to failure due to distribution shift and unseen classes. The paper proposes to adapt various loss terms used in other SSL tasks. In addition, the paper focuses on performing SSL on the feature-level representations, where the author provides theoretical analysis to argue the superiority of contrastive loss over conventional BCE loss. Experiments combine the proposed CONTROL technique with two existing baselines and show noticeable improvements in the accuracy of both seen and unseen classes.
Strengths: - The paper investigates a somewhat interesting task setting, termed open-world SSL, which is of interest for the deployment of vision models in the wild.
- The presentation for the benefit of using the contrastive loss over conventional BCE loss is good with theoretical analysis, which provides a good motivation for introducing contrastive loss to open-world SSL, and can potentially facilitate future research.
- The paper provides good experimental results where the proposed method is combined with two previous baselines on CIFAR-10, CIFAR-100, and the ImageNet-100 datasets. The ablation study of CONTROL on CIFAR-100 also justifies the motivation for different introduced loss terms. The final analysis on the improved 'recall' rate of samples from unseen classes also provides good motivation for the proposed method
Weaknesses: - The main weakness of the paper lies in the narration of the paper, which makes it hard for a broader audience to capture the contributions of the paper. In many parts of the paper, the writing is focused on *what* is done but not *why* this is better than alternative methods. This makes the paper seem like a technical report rather than a research paper. A good paper is expected to provide insights to other researchers on 'why' the method works and how it can help with future research and real-world applications. This issue is particularly concerning in the introduction section, which is supposed to provide readers with high-level motivation of the methods. However, in the introduction, the third paragraph lists BCE-based methods and non-BCE methods without discussing when each of the methods is preferred and why each is motivated. Though the fourth paragraph attempts to argue for the superiority of BCE-based methods for open-world SSL, it again does not talk about high-level motivation for BCE-based methods. Instead, it directly dives into the details of similarity functions and talks about 'pulling closer sample pairs'. Such a discussion misses out a lot of context and causes confusion on what are the actual contribution of the paper. Also, the highlighted question in the introduction section seems to overlap greatly with the contribution with OpenCon. The only difference is that the authors claim CONTROl is 'unified' and 'open-world', but there is no further analysis on these two factors and how CONTROL differentiates itself from OpenCon.
- The paper seems to miss out on the addition of CONTROL to an important baseline OpenCon. OpenCon also proposes to apply contrastive learning for SSL. The paper argues that OpenCon is 'incapable of sustaining continuous optimization of representations during the process of semi-supervised learning'. However, from the final table 1 and table 2, the performance of OpenCon is pretty close to NACH+CONTROL, which somewhat undermines the claim of the novelty and contributions of CONTROL. A more convincing experiment would be to extend OpenCon with techniques proposed in this paper.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 2 fair