Title: Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery
Venue: NeurIPS 2025
Accepted: yes
Keywords: Generalized Category Discovery

Neural Collapse
Abstract: Generalized Category Discovery (GCD) focuses on classifying known categories while simultaneously discovering novel categories from unlabeled data. However, previous GCD methods face challenges due to inconsistent optimization objectives and category confusion. This leads to feature overlap and ultimately hinders performance on novel categories. To address these issues, we propose the Neural Collapse-inspired Generalized Category Discovery (NC-GCD) framework. By pre-assigning and fixing Equiangular Tight Frame (ETF) prototypes, our method ensures an optimal geometric structure and a consistent optimization objective for both known and novel categories. We introduce a Consistent ETF Alignment Loss that unifies supervised and unsupervised ETF alignment and enhances category separability. Additionally, a Semantic Consistency Matcher (SCM) is designed to maintain stable and consistent label assignments across clustering iterations. Our method significantly enhancing novel category accuracy and demonstrating its effectiveness.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles inconsistent objectives in Generalized Category Discovery by pre-assigning a fixed Simplex Equiangular Tight Frame (ETF) of prototypes for all classes and aligning both labeled and unlabeled samples to that frame via a unified “ETF alignment” loss. It augments periodic clustering of unlabeled data (unsupervised ETF) with supervised alignment of known-class embeddings and introduces a Semantic Consistency Matcher (SCM) that enforces one-to-one cluster mappings across iterations to stabilize pseudo-labels. Extensive theory (Neural Collapse proofs), hyperparameter studies, ablations, error bars over five seeds, and runtime/parameter efficiency analyses on six benchmarks demonstrate substantial gains, especially in novel-class accuracy, over previous GCD methods.\n\nSTRENGTHS AND WEAKNESSES:\n### *Strenghts*\n- #### S1 **Principled Geometry via Neural Collapse.** Pre-assigned ETF prototypes enforce optimal inter-class angles and intra-class compactness. Theoretical grounding justifies the fixed simplex structure for both known and novel classes.\n\n- #### S2 **Unified Supervised / Unsupervised Loss.** A single “Consistent ETF” loss seamlessly blends labeled alignment and high-confidence unlabeled alignment, leading to promising results.\n\n- #### S3 **Thorough Evaluation.** Benchmarks on generic and fine-grained datasets, with GT-K and estimated-K settings, error bars over five seeds. Full pseudocode, hyperparameters, runtime/parameter tables, and feature visualizations provided in the appendix.\n\n### *Weaknesses*\n- #### W1 **Rigid Prototype Structure.** Fixed ETF assumes ideal simplex geometry; real data may require prototype flexibility. No mechanism to adjust or refine prototypes when feature distributions deviate.\n\n- #### W2 **Scalability of SCM's Permutation Search.** SCM’s exact permutation over K clusters might scales poorly as K grows. Approximate or hierarchical matching strategies are not explored for large-scale or streaming discovery.\n\n- #### W3 **Experiment on Other Vision Foundation Model.** All experiments use DINO ViT-B/16. It’s unclear if gains hold with stronger DINO v2, other stronger backbones.\n\n- #### W4 **Typo.** Notation inconsistencies (e.g., undefined qᵢ in Eq. 3), typos in figure 8 & 9 captions (“processs”).\n\nQUESTIONS:\n### *Questions*\n- #### Q1 **Adaptive Prototypes.** Could small learnable offsets to ETF prototypes improve fit to non-ideal manifolds?\n\n- #### Q2 What are the advantages of applying ETF over non-Euclidean geometry representation such as hyperbolic representation?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to improve GCD accuracies by providing an optimal geometric structure that maximizes inter-class separation and maintains intra-class compactness. These fixed prototypes ensure consistent optimization goals for both known and novel categories, reducing category confusion coupled with semantic consistency matcher. The paper further adopts comparative representation learning to ensure consistent training objective for both known and unknown classes. Extensive experiments demonstrate the effcacy of their method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n(1) The proposed method follows a simple but effective GCD pipeline by improving GCD accuracies via representation learning.\n\n(2) The improvement over previous methods are significant, demonstrating their method's efficacy.\n\n(3) The overall writing is good and easy to understand.\n\n(4) Moreover, authors conduct extensive experiments and theoretical proof to verify their method.\n\nWeaknesses:\n\n(1) Based on the paper's analysis, initial estimation of K may have significant affect on final GCD accuracies. That is because the initial K determines how SCM matches pseudo labels across different epochs. However, the estimation of K may be more accurate after several epochs of training. Therefore, is it possible to further polish your method by adjusting K after several epochs of training ? E.g., after several epochs of training, we re-estimate K and start a new NC-GCD training pipeline.  \n\n(2) The authors did a great job in verifying the proposed method. Extensive experiments have been conducted and all implementation details are introduced. However, I think some important implementation details and conclusions should be moved to the main paper. e.g., the setting of periodic clustering should be demonstrated in the main paper and the results in appendix Sec. B.4 \n\n(3) Minor: Text fonts in Fig.1 and 2 are too small.\n\nQUESTIONS:\nI like this paper due to its abundant experiments and solid improvements over several GCD benchmarks. My main concern lies in  \"weaknesses\"-(1).\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper proposes NC-GCD, a Neural Collapse-inspired framework for Generalized Category Discovery, integrating supervised and unsupervised ETF alignment and a Semantic Consistency Matcher to enhance class separability and achieve state-of-the-art performance on multiple GCD benchmarks.

STRENGTHS AND WEAKNESSES:
Strengths:

1. This paper is well-written and easy to follow.

2. The problem investigated is realistic and complex.

3. The proposed approach outperforms other competitors on various datasets.

Weaknesses:

1. The idea of this paper is not novel, as there is ETF-based method prposed in the GCD / Open-World SSL field[1]. The paper should discuss in detail the differences from this uncited paper, and the key losses appear to be the same in my view.

2. The framework of this paper is designed based on the SOTA method CMS. Given that CMS is already strong in terms of predicting class numbers, accuracy, etc., how would the performance improve if this framework incorporates more other GCD methods or weaker GCD methods?

[1] Targeted Representation Alignment for Open-World Semi-Supervised Learning, CVPR 2024.

QUESTIONS:
Please see the weaknesses.

LIMITATIONS:
This paper needs to clarify its differences from the paper[1] and articulate its own contributions to better influence the community.

[1] Targeted Representation Alignment for Open-World Semi-Supervised Learning, CVPR 2024.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

comments: SUMMARY:
This paper addresses generalised category discovery (GCD). As its main contribution, it proposes to use fixed prototypes determined by maximally seperated simplex point s along an equiangular tight frame, instead of learned ones. To this end a new loss is proposed that combines supervised and self-supervised data. The paper evaluates the method on a range of GCD benchmarks and finds favorable performances.

STRENGTHS AND WEAKNESSES:
+ strong results on classic GCD benchmarks
+ relatively simple method
+ clear writing	

- results shown only for a single backbone
- no results for unbalanced datasets, potentially overfitting to equal-class distribution nature of the evaluated datasets
- no analysis whether the points actually do follow the NC hypothesis and end up near the simplex points
- (minor) figures have too small fontsizes

QUESTIONS:
* How does the method perform with other non-DINOv1 backbones and other sizes?
* the method has a trade-off between old and new classes performance with the hyperparameter alpha. Is there a way to determine this per dataset in an unsupervised way?
* In what way does the SCM address clustering instability?
* What if you use random fixed points instead of equiangular ones? Similar to "Unsupervised Learning by Predicting Noise" paper 
* How does the method perform on more realistic unbalanced GCD datasets?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents NC-GCD, a Neural Collapse-inspired framework for Generalized Category Discovery. The key idea is to pre-assign Equiangular Tight Frame prototypes for both known and novel categories, aiming to achieve consistent optimization objectives and improve class separability. The framework introduces (1) supervised and unsupervised ETF alignment with a consistent loss function and (2) a Semantic Consistency Matcher to stabilize cluster assignments across iterations. Extensive experiments on multiple benchmarks demonstrate improvements over prior state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n+ The proposed method looks novel. They leverage Neural Collapse insights to enforce consistent category separation.\n+ Experimental results across six benchmarks are thorough, including comparisons to strong baselines and ablation studies demonstrating the contributions of individual components.\n+ The motivation and presentation of the proposed method are clear with good illustrations and visualizations of feature separability.\n\nWeaknesses\n- All experiments rely exclusively on DINO ViT-B/16 as the backbone. This raises concerns about whether improvements generalize to different architectures.\n- Several recent strong baselines for GCD are missing in the comparison below. Including these would strengthen claims of state-of-the-art performance.\n- Although the method is well motivated, the reliance on ETF prototypes is not entirely novel (prior works have leveraged Neural Collapse in other contexts), which somewhat limits originality.\n- A minor thing. Citation formatting does not follow NeurIPS style (should be [1] instead of (1)).\n\n\n[1] Kim, Hyungmin, Sungho Suh, Daehwan Kim, Daun Jeong, Hansang Cho, and Junmo Kim. \"Proxy anchor-based unsupervised learning for continuous generalized category discovery.\" In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16688-16697. 2023.\n[2] Cendra, Fernando Julio, Bingchen Zhao, and Kai Han. \"Promptccd: Learning gaussian mixture prompt pool for continual category discovery.\" In European Conference on Computer Vision, pp. 188-205. Cham: Springer Nature Switzerland, 2024.\n[3] Park, Keon-Hee, Hakyung Lee, Kyungwoo Song, and Gyeong-Moon Park. \"Online Continuous Generalized Category Discovery.\" In European Conference on Computer Vision, pp. 53-69. Cham: Springer Nature Switzerland, 2024.\n[4] Rypeść, Grzegorz, Daniel Marczak, Sebastian Cygert, Tomasz Trzciński, and Bartłomiej Twardowski. \"Category adaptation meets projected distillation in generalized continual category discovery.\" In European Conference on Computer Vision, pp. 320-337. Cham: Springer Nature Switzerland, 2024.\n\nQUESTIONS:\n1. All experiments use DINO ViT-B/16. Could the authors show results with other backbone architectures (e.g., ResNet variants) to validate generalization?\n\n2. The paper does not compare with several recent works. Please add the recent SoTA works and compare those with the proposed method.\n\n3. Please update references to use NeurIPS [number] format consistently throughout the paper.\n\nLIMITATIONS:\nThe authors discussed potential limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}