Kati8/ICLR_2023_2025 huggingface dataset:
authors: Jingyi Cui
authors: Yi-Ge Zhang
authors: Yisen Wang
reviews: 
Comments: This paper introduces a contrastive loss designed to enhance the performance of open world semi-supervised learning (OWSSL). Specifically, it proposes three distinct loss functions tailored for nearest neighbor contrastive learning, supervised contrastive learning, and unsupervised contrastive learning. The primary aim of these functions is to address and mitigate the representation collapse issue. The method presented has achieved state-of-the-art performance on benchmark datasets including CIFAR-10, CIFAR-100, and ImageNet-100.
Strengths: 1. The proposed approach is straightforward and easy to understand.
2. The method's efficacy has been demonstrated across various datasets.
Weaknesses: **1. Novelty Concerns**:
The proposed method essentially adds three types of contrastive losses to the existing OWSSL loss. Of these, the supervised contrastive loss is merely a standard contrastive loss. Additionally, both the NN contrastive loss and the SimAll contrastive loss that utilizes augmented images as positive keys lack distinctive features compared to existing research [A, B].

[A] With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations, ICCV 2021. \
[B] Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views, CVPR 2022.

**2. Overall Paper Completeness**:
The completeness of the paper is generally lacking. The proof provided in Section 4.1 for motivation is not rigorous, with some intermediate steps omitted. And there are three hyperparameters related to the loss. However, the paper does not provide details on the values chosen for these parameters, nor does it discuss their sensitivity or robustness through experiments.

**3. Reproducibility Concerns**:
The absence of a reproducibility statement raises doubts about the possibility of replicating the claimed state-of-the-art performance.
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: 1 poor
Clarity: 2 fair
reviews: 
Comments: The paper presents CONTROL, a contrastive learning framework designed to improve the performance of open-world semi-supervised learning (SSL) algorithms. CONTROL addresses the presence of unseen classes by integrating three optimization objectives: nearest-neighbor contrastive learning, supervised contrastive learning, and unsupervised contrastive learning. Experimental evaluations on CIFAR and ImageNet show promising improvements over state-of-the-art methods.
Strengths: + The proposed three objectives are interesting and helpful.  
+ The proof and derivation of the objectives are comprehensive and easy to understand. 
+ Extensive evaluations and comparisons to baselines show promising results.
Weaknesses: The writing of this paper is kind of informal, some examples include:
- The whole Section 3 missed a lot of details, lacking the basic problem setting, and key components of relied models, which makes the whole method hard to understand. 
- Fig 1 and Fig 4 should be further polished. There are also overlaps between those two figures. 

Next, It's hard for me to evaluate the contribution of the three loss functions, as all of them have been somehow explored in other areas before, and open-world semi-supervised learning is a new task. I have no sense if the contribution is huge or minor, so as the performance gain.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 2 fair