Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This work proposes cross-instance positive relations that introduce a selective neighbor clustering module to help generate pseudo labels for contrastive learning. It can promote using both the labeled and unlabelled data to help more representative representation. It presents experiments on several benchmarks to demonstrate its performance.

strength_and_weaknesses: Strength
1. I idea using both labeled and unlabelled data is intuitive.
2. The performance improvement is quite impressive, e.g., up to 10% accuracy improvement in some settings.
3. The paper is well-written and easy to follow.

Weaknesses
1. It seems the key contribution is the SNC which can help generate pseudo labels. The authors claim that SNC is inspired by and similar to FINCH. However, the difference with FINCH is not well clarified. 
2. The authors use SNC to generate pseudo labels. I wonder how to ensure the accuracy of generated labels. It may hurt model training if generating many wrong labels. 
clarity,_quality,_novelty_and_reproducibility: The originality should be better clarified.

summary_of_the_review: I think the overall idea is OK and the experiments are quite convincing. The contribution should be better clarified.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper considers the problem of generalized category discovery (GCD). To solve this problem, the authors propose a positive-pair mining approach for facilitating the contrastive learning, which encourages the network learn more discriminative representation. Extensive experiments on several GCD datasets show the benefit of the proposed method. 
strength_and_weaknesses: Strength 

+ This paper studies a new problem and practical problem in the community.

+ This paper is well written and easy to follow.

+ An effective approach is proposed for GCD, which largely improves the sota performance.

+ Extensive experiments are provided to show the benefit of the proposed method.



Weaknesses

- Despite the high performance, the novelty of the proposed method is somewhat limited. Both clustering-based positive pair mining and supervised contrastive learning based on the selected pairs are not new techniques in the community.

- The description of Sec.3.3 and Sec.3.4 is hard to understand.

- The improvement over the original FINCH sometimes is limited. For example, in Table 5, when applying SNC on only unlabeled data, it should be mostly similar to FINCH with unlabeled data. However, the full method does not produce a clear improvement on the SNC with u-u.

- The memory cost comparison between Cip and GCD should be provided in Table 3.



clarity,_quality,_novelty_and_reproducibility: Clarity and Quality: The overall writing of this paper is good. However, the Sec3.3 and 3.4 are hard to understand.

Novelty: The proposed clustering and learning framework is not very novel.

Reproducibility: Algorithms are provided in the paper. However, the proposed method is quick complex. I think it is not easy to reproduce the method without the source code.
summary_of_the_review: This paper studies a new task and proposes an effective method for this task, which achieves new SOTA performance. However, the novelty of the proposed framework is somewhat limited. In addition, compared to the original FINCH method, the improvement of the proposed method is sometime limited.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper tackles the task of generalized class discovery where both labelled and unlabelled data can be encountered during inference. The authors proposed a semi-supervised hierarchical clustering algorithm to solve this problem, which is built upon a known clustering method. The proposed approach is equipped with the capability of generating reliable pseudo labels as well as fast estimating unseen class numbers. The proposed method is evaluated on several popular benchmarks, which demonstrates promising results.
strength_and_weaknesses: Strength:

- The paper is well organized and easy to follow.
- The proposed method is new and demonstrated to be effective.

Weaknesses:

- The propsed method is a simple extension of the existing hirarchical clustering algorithm, with intuitive modification that is not properly justified theoretically. For instance, why should samples linked by chains? Why should chains be connected under certain regulations? Why is it significant and necessary? These are just proposed but not well justified. 

clarity,_quality,_novelty_and_reproducibility: The overall quality is good. The paper is clearly written and the proposed method is novel.
summary_of_the_review: A well written paper with less theoretical contribution.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Based on the collected information from all reviewers and my personal judgment, I can make the initial recommendation on this paper, ** reject**. Here are the comments that I summarized, which include my opinion and evidence.

**Research Problem**

The authors study the open-world clustering problem, where the dataset is partially labeled and the unlabeled data contain instances from both seen and unseen categories. 

**Motivation**

The motivations come from the drawback of one state-of-the-art algorithm (Vaze et al., 2022b). (1) Vaze’s method considers labeled and unlabeled data independently, (2) Vaze’s method requires a known category number, and (3) Vaze’s method is inefficient. The second point is not true. Vaze’s method can also estimate the unknown category number for the unlabeled data based on a semi-supervised K-means clustering. For the third point, there exist several estimation methods, which do not need multiple runs. The authors constrain their scope into a small range. For the first point, the motivations are not strong enough and make this paper an incremental work. 

**Philosophy**

For the first motivation, the authors aim to explore all possible interactions between labeled and unlabeled instances. For the unlabeled data, pseudo labels from their designed clustering algorithms are used to generate cross-instance positive relations. This is very straightforward. Moreover, it might suffer from self-bias due to no supervision from unlabeled data. Such pseudo labels might further enforce the cross-instance positive relations derived from the pseudo labels. 

**Presentation**

In general, this paper is easy to follow. One minor issue: I suggest giving a definition of the cross-instance positive relations.

**Technique**

Several reviewers and I have concerns about the technical novelty. Most techniques are built on existing frameworks. For the new content in Section 3, the novelty is limited and incremental compared to the existing literature. 

**Experiments**

The biggest advantage of this paper is that the experiments are extensive and solid. Very nice.

No objection was raised from the reviewers on the rejection recommendation.
justification_for_why_not_higher_score: Although the experiments are rich, the novelty is limited, which makes this paper difficult to reach the bar of ICLR.
Decision: Reject