Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization
Authors: Duo Liu
Zhiquan Tan
Linglan Zhao
Zhongqiang Zhang
Xiangzhong Fang
Weiran Huang
Venue: ICML 2025 poster (2025)
Keywords: Generalized Category Discovery，Distribution Regularization
Primary area: general_machine_learning->unsupervised_and_semisupervised_learning

Abstract: Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones, where the unlabeled set consists of both base and novel classes. 
Since clustering methods are time-consuming at inference, parametric-based approaches have become more popular. 
However, recent parametric-based methods suffer from inferior base discrimination due to unreliable self-supervision. 
To address this issue, we propose a Reciprocal Learning Framework (RLF) that introduces an auxiliary branch devoted to base classification. 
During training, the main branch filters the pseudo-base samples to the auxiliary branch. 
In response, the auxiliary branch provides more reliable soft labels for the main branch, leading to a virtuous cycle. 
Furthermore, we introduce Class-wise Distribution Regularization (CDR) to mitigate the learning bias towards base classes. 
CDR  essentially increases the prediction confidence of the unlabeled data and boosts the novel class performance. 
Combined with both components, our proposed method, RLCD, achieves superior performance in all classes with negligible extra computation. 
Comprehensive experiments across seven GCD datasets validate its superiority.
Our codes are available at https://github.com/APORduo/RLCD.

Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization
Duo Liu
Zhiquan T
Linglan Zhao *1 Zhongqiang Zhang'
Xiangzhong Fang'
Weiran Huang 9 134
Abstract
(a) Parametric GCD: inferior discriminstion
Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones, where the unla- beled set consists of both base and novel classes. Since clustering methods are time-consuming inference, parametric-based approaches have be- more popular. However, recent parametric- based methods suffer from inferior base discrimi- nation due to unreliable self-supervision. To ad- dress this issue, we propose a Reciprocal Learn- ing Framework (RLF) that introduces an auxil- iary branch devoted to base classification. During training, the main branch filters the pseudo-base samples to the auxiliary branch. In response, the auxiliary branch provides more reliable soft labels for the main branch, leading to virtuous cycle. Furthermore, we introduce Class-wise Distribu- tion Regularization (CDR) to mitigate the learn- ing bias towards base classes. CDR essentially increases the prediction confidence of the unla- beled data and boosts the novel class performance. Combined with both components, our proposed method, RLCD, achieves superior performance in all classes with negligible extra computation. Comprehensive experiments across seven GCD datasets validate superiority. Our codes are available at https:/giitab.oom///POduuuuu
Labeted
Unlsbeled
Image Encoder
CLS
Self- suee--uueeiiiionn
All Class Logits
(b) Ours: enhanced base discrimination
Labeled
Unisbeled
CLS
Sel Self-supervision
Image Encoder
supervision
AUX
Base Class Logits
Figure (a) Parametric Generalized Category Discovery (GCD) methods rely on self-supervision for clustering unlabeled data but exhibit weak base class discrimination.
(b) Our approach intro- duces an auxiliary branch specialized in base class classification. providing more reliable base logits to the main branch and thereby significantly improving base discrimination.
Dosovitskiy, 2021). Generally, the models rely on abundant annotated data in closed scenario where the unlabeled data share the same classes with the labeled training data. However, these models have limitations in the real-world scenario where unlabeled data comes from unknown classes. In this way, Category Discovery (CD) has garnered atten- tion in the machine learning community. Initially, Han et al. (2019) propose Novel Class Discovery (NCD), which is designed to cluster novel class data with the assistance of labeled data exclusively. However, NCD assumes that the unlabeled data all belong novel classes, which is unrealis- tic in practical scenarios. Recently, Generalized Category Discovery (GCD) (Vaze et al., 2022b) has emerged and it allows the unlabeled data spanning both base and novel cat- egories. Compared to the NCD task, GCD more practical and challenging real-world scenarios.
1. Introduction
With the development of deep learning in recent years, mod- els can perform well traditional tasks such as image recognition (He al., 2016; 2017; Vaswani al., 2017:
"School Electronic Information and Electrical Engineer- ing. Shanghai Jiao Tong University 'Department of Mathemat- ical Sciences, Tsinghua University 'Shanghai Innovation Insti- tute *State Key Laboratory of General Artificial Intelligence, BIGAL. "Project lead. "Correspondence to: Weiran Huang <weiran. huang com.
Vaze et al. (2022b) first define the GCD problem and tackle it using contrastive learning along with the semi-supervised k-means clustering method. Wen et al. (2023)further pro- pose an effective parametric framework, SimGCD, which outperforms the clustering methods with reduced inference time. Due to its effectiveness, the parametric framework
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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GCD via reciprocal learning and class-wisc distribution regularization
CIFAR100
Stanford Cars
propose a Class-wise Distribution Regularization (CDR) technique. Specifically, CDR involves calculating the ex- pected distribution for each category based on mini-batch predictions. Then, CDR loss promotes expectation consis- tency between two views of the mini-hatch and boosts pre- diction confidence. Since each class can be treated equally, CDR effectively mitigates the bias and boosts novel class performance. By integrating CDR into the RLF, our method, named RLCD, obtains substantial improvements.
Oracle Basn Accurcay (56)
Ours
Crecle Basa Accuracy (%6)
92-
Ours
-66
SusRul
85-
LogoGCD
SupRer
84-
LegeGC3
9m3CD
83
76
50
100
150
200
0
50
100
150
200
Epoch
Epoch
Figure 2. Comparison of the oracle base class accuracy hetween SimGCD, LegoGCD, and our method. SimGCD and LegoGCD exhibit poor performance, falling behind the supervised reference (SupRef). Contrarily, our method exhibits enhanced discrimination, even surpassing SupRef.
Our key contributions can be summarized as follows:
We define the oracle base class accuracy (OB) as a metric to assess the base class discrimination of GCD models, revealing the inferior discrimination of para- metric GCD methods.
We design a novel reciprocal learning framework to promote base class discrimination and a class-wise distribution regularization loss to improve novel class performance.
has become popular in GCD research. Wang et al. (2024) design two-stage framework on the pre-trained SimGCD model that introduces both global and spatial prompts to fine- tune the model. LegoGCD(Caao et al., 2024) observes that SimGCD suffers catastrophic forgetting of base classes, and they propose a novel regularization to address it. Despite the significant advancements in parametric methods, Fig. reveals a key limitation: these methods rely exclusively on self-supervision, leading to suboptimal base discrimination.
Experimental results on seven GCD datasets show that the proposed method consistently outperforms of the-art approaches in most scenarios.
2. Related Works
To quantitatively reveal the main limitation in existing works, we define the oracle base accuracy (OB) for evalu- ating base diserimination. OB solely considers base-class prediction and calculates the accuracy of unlabeled base data utilizing the prototype classifier. As shown in Fig. 2, both SimGCD and LegoGCD lag behind the supervised- only reference (SupRef), which exclusively utilizes labeled data for training. This disparity primarily arises from the unreliable soft labels in self-supervised learning.
Semi-Supervised Learning (SSL) is a prominent area in machine learning that addresses the challenge of train- ing models with limited labeled data. Pseudo Label (Lee et al., 2013) iteratively assigns pseudo labels for unlabeled data, which join the labeled set for further training. Mean- teacher (Tarvainen & Valpola, 2017), UDA (Xie et al., 2020), Fixmatch (Sohn al., 2020) adopt confidence threshold to generate pseudo labels on weak augmented samples and utilize it to supervise strongly augmented samples, and DST (Chen et al., 2022) proposes an adversary framework to refine pseudo labels. ConMatch (Kim et al., 2022) adds self- supervised features regularization, while SimMatch (Zheng et al., 2022) extends consistency to the semantic and in- stance levels. Prevailing semi-supervised methods widely adopt threshold- based pseudo-label learning during training. However, this mechanism faces significant limitations when unlabeled data includes samples from unknown classes.
To promote base discrimination, we design a Reciprocal Learning Framework (RLF). In particular, we insert an aux- iliary token named AUX in the model architecture. Then AUX is concatenated with the CLS token and image feature tokens to form the input of the final block. Subsequently, the corresponding AUX output is dedicated to base-only classifier while the CLS output is designated for the all-class classifier. During training, the main branch filters pseudo- base samples, which are predicted to the base classes, and directs them to the auxiliary branch. In feedback, the auxil- iary branch provides reliable base class distribution to the main branch. This collaboration between the two branches contributes to more robust base predictions, improving base- class discrimination and overall accuracy. Benefiting from parallel computation between tokens, the extra computation cost is negligible.
Novel Class Discovery (NCD) aims to recognize novel classes in unlabeled data by exploiting knowledge from known classes. (Han et al., 2019) first proposes the NCD problem and addresses utilizing a two-stage training strat- egy. (Han et al., 2020) employ rank statistics to find posi- tive data pairs and pull them closer. OpenMix (Zhu et al., 2023) generates virtual samples by MixUp between labeled and unlabeled data, guiding the model to resist noisy la- beled data. (Zhong et al., 2021) proposes neighborhood con- trastive learning to aggregate pseudo-posine pairs. (Fini et al., 2021) introduces a unified objective framework with
However, the reciprocal framework may incur learning bias toward base classes that more novel samples are misclassi- fied into the base classes. To alleviate the above bias, we
2

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GCD via reciprocal learning and class-wisc distribution regularization
CLS
CLS
L CDR
+ Frozen
Aug 1
Transformer Block [O]
Transformer Block [1.]
Transformer Block [L]
L mainn
Classes
Tunable
Sample
P Probability
ilter
Distill
Aug 2
Token
Classifier
L DAX
Base Protos
AUX
AUX
L CDR
Novel rotos
Base Classes
F ilter
Distill
CDR
Base ovel Pred
Base Pred
Prob 1
Prob 2
N MXX
Class Expectation
Max Sim
Pseudo Novel
Pseudo Base
v
L KL
K
Figure 3. Overview of our method. We insert an auxiliary token AUX before the last block of VIT backbone. The final AUX feature utilized for the base-only classifier while the CLS feature is assigned the all-class classifier. The main branch filters the pscudo-base samples to the aux branch for better hase class learning. response, the auxiliary branch provides the main branch with the refined base class distribution. Class-wise Distribution Regularization (CDR) boosts novel performance by maximizing the similarity between class-wise probability matrices m from two views.
the Sinkhorn- KnohporrnnKppppppooo algorithm, allowing cross- cross-entropy to operate on both labeled and unlabeled sets. CRNCD (Gu al., 2023) conducts a clast-celationship distillation ap- proach improve novel-class performance. However, this distillation shows inferior performance GCD. Unlike CRNCD, we propose novel one-stage distillation method tailored for GCD.
introduces a two-stage strategy that combines the global and spatial prompts to further finetune the SimGCD model. Lin et al. (2024) design teacher-student attention alignment strategy promote GCD performance. LegoGCD (Cao et al., 2024) finds that SimGCD suffers from catastrophic forgetting in training and solves it by adding regularization to potential known class samples. While parametric-bass methods achieve great GCD performance, they often expe- rience degraded base discrimination. To address this issue, we propose Reciprocal Learning Framework (RLF) that provides more reliable base pseudo-labels and effectively strengthens base performance.
Generalized Category Discovery (GCD) is to cluster unla- beled images by leveraging the base knowledge from labeled images, where the unlabeled set comprises both base and novel classes. (Vaze al., 2022b) formulates the GCD problem and conducts contrastive training pre-trained ViT model (Dosovitskiy, 2021) with DINO (Caron et al., 2021), followed by semi-supervised k-means clustering. Re- cent works have extended GCD to settings such as active learning (Ma et al., 2024b) and continual learning (Ma et al., 2024a). CiPR (Hao et al., 2024) designs a novel contrastive learning method by exploiting cross-instance positive rela- tions in labeled data and introducing hierarchical cluster- ing algorithm. GPC (Zhao et al., 2023a) applies Gaussian mixture models that learn robust representation and estimate the novel class number. Wen et al. (2023) propose a para- metric framework that trains a prototype classifier to fit all categories. SimGCD utilizes mean-entropy regularization to automatically find novel classes. As SimGCD boots GCD performance with lower inference latency, the parametric framework becomes popular. SPTNet (Wang et al., 2024)
3. Method
3.1. Preliminaries
Problem formulation. Generalized Category Discovery (GCD) aims to adaptively cluster unlabeled data utilizing the knowledge from labeled data. GCD is built upon the open- world dataset, which compromises two subsets: labeled dataset D [(x,, v.)} X' x y and unlabeled dataset D ((,, v:}} E X x ym. Formally, ) is subset of )"", and ) spans all categories. Following previous research, the number of | is assumed as the prior. GCD adopts a transductive training strategy in which all the samples are involved in the training process.
Parametric clustering. (Wen et al., 2023) proposes effi-
3

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GCD via reciprocal learning and class-wisc distribution regularization
cient parametric framework that builds prototype classifier for clustering. Specifically, the classifier weight is the set of prototypes C' = [ci,.... cK). where K is the total number of prototypes. Given an image 2; the model correspond- ingly output feature fmx)), and the probability of category is denoted as:
base classification, the unlabeled base data is de- fined Phase
(e),,, 16) 'D", ; E )"} The oracle base accuracy is defined as ACCOB | Le... 1 (ū/ = ys). where yi is the predicted base class result. As depicted in Fig. 2, prevailing para- metric methods exhibit unsatisfactory oracle base accuracy, falling behind the supervised-only reference.
exp (cos (f (a::) Cx) ) TTa) Σx exp (cos (J (1x.) , Cx) /TT))
pi (k)
To this end, we propose a one-stage Reciprocal Learning Framework (RLF). As shown in Fig. 3, we insert the auxil- iary token AUX before the last block, concatenating it with CLS and feature tokens to form the input. The final AUX feature is utilized for the base-only classification, while the CLS feature is assigned to the all-class classifier. Different from the CLS feature, which is unique to each image, the AUX token is trainable parameter shared across all training samples.
where cos denotes the cosine similarity between two vectors and Tu is the temperature scalar. Similarly, the shrink prob- ability q; can be derived by substituting T, with smaller To. Subsequently, SimGCD adopts the cross entropy loss (e p) = - Ex (( log (()) to regularize the probabil- ity self coffisonsitencc between two views of an image. For i-th image, the loss is formulated as:
1 golf _o (d'. p.) + Los (q., P'),
1
During the training procedure, the main branch is akin to generic parametric clustering. Besides, the main branch filters the pseudo-base class samples to the auxiliary branch according to the prediction result, i.e., if sample is pre- dicted to belong to the base classes, it will also be involved in the auxiliary branch. In response, the auxiliary branch dis- tills the base class prediction of pseudo-base samples to the main branch. The collaboration between the two branches effectively enhances base discrimination, mitigates the influ- ence of noise labels, and facilitates the model in acquiring improved representations.
(2)
where pt and qf are the prototype probabilities of an- other view. Additionally, SimGCD employs a mean- entropy maximization regulariser for clustering: H(p) = -E. (t) where p is the mean predicted probabil- ity of all the samples. The supervised loss for the labeled data is the sum of cross- cross-entropy and supervised contrastive learning losses (Khosla et al., 2020):
exp (cos(f (I:), f(x))) /Te)
(()) con
log
Note that most of the training samples will be predicted as the base classes initially, and the auxiliary branch also incorporates abundant novel samples at the same time. To this end, the auxiliary branch adopts seff-super learning and supervised learning, rather than threshold-based semi- supervised methods. Furthermore, utilize the maximum probability as the uncertainty weight for each pseudo- base sample in the cross-branch distillation. The distillation loss for a pseudo-base sample i is denoted as:
PPP
En exp (( (ma), I (ax)) (1%))//T) /Ta)
PEP.
c(O = Lee (2/,: P:) + C(1 sup
cum!
(3)
where y, is the one-hot distribution associated with yi, To denotes the temperature scalar for supervised contrastive learning and the P, is the positive index set sharing the same label as z;- While SimGCD applies InfoNCE (Oord et al., 2018) loss in the training, we found the loss tends to push apart same-class features, which conflicts with the SupCon loss and impairs feature discrimination. Consequently, we chose to remove InfoNCE in our approach for better dis- crimination. Overall, the parametric clustering loss Lats is the average per-sample combination of supervised loss, self-consistency loss, and entropy regularization loss:
cl max(p..)) LKI (Pt. Po.s),
where pa P.x.Ps iary and main branch, LKL is the standard KL-divergence loss, and the auxiliary probability is detached in the distilla- tion. Consequently, the loss functions of the two branches can be presented as:
Pd is the base class distribution from the auxil-
Lets = Suuup + (1 A) - ell ep)),
(4)
Emain = Lets + aLdis Laux = -sup + Esett.
(6)
where X is the balance weight belonging to [0.1] and e is the scalar to control entropy regularization.
where a controls the distillation strength.
3.2. Reciprocal Learning Framework
3.3. Class- Wise Distribution Regularization.
Motivation. While SimGCD demonstrates greater ef- fectiveness than clustering methods, falls short in base class discrimination. Specifically, when focusing
While the proposed reciprocal framework can effectively improve base class discrimination, it still shows inferior performance in the novel classes. This is primarily due to
4

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GCD via reciprocal learning and class-wisc distribution regularization
CUR200
The effectiveness of CDR is evidenced in Fig. 4, it leads higher number of predicted novel class samples, reducing learning bias and boosting prediction confidence. Further- more, the CDR loss operates independently of ground- truth labels and is compatible with both branches. When applied to the auxiliary branch, CDR also benefits base class learn- ing with minimal impact on the novel class performance of the main branch. By integrating the CDR loss into the reciprocal framework, the overall loss is summarized
APrddesed Novel Samples
Mean Prodiction Confidonce
W/o CDR
w/ GDR
0..99
0.68
w/ CDR w/o CDR
0.64
GT
0.5%
.656
100
AI
Base
Novel
Epoch
Figure 4. The efficacy of CDR is evident in aspects. Left: CDR induces more predicted novel class samples. Right: CDR contributes to higher prediction confidence.
= Lmain Laux + BLCDR:
(9)
where 3 controls the regularization weight, After the train- ing procedure, we abandon the auxiliary classifier and only keep the main branch for evaluation. As result, the infer- ence latency difference from SimGCD is negligible.
the cross-branch distillation being confined base class distributions, resulting in learning bias where training samples are more likely to be recognized base classes. Fig. 4 (a) shows that the predicted novel samples lag behind the ground truth number. To mitigate the learning bias, we propose a novel Class-wise Distribution Regularization (CDR) shown in the bottom right of Fig. 3.
4. Experiments
4.1. Experimental Setup
Datasets. Following previous works, evaluate our method on seven different GCD datasets. Those consist of generic image recognition datasets CI- FAR10/100 (Krizhevsky et al., 2009) and ImageNet- 100 (Tian et al., 2020); Semantic Shit Benchmark (SSB) (Vaze et al., 2022a) datasets: CUB200 (Wah et al., 2011), Stanford Cars (Krause al., 2013), and FGVC- Aircraft (Maji al., 2013); large-scale fine-grained dataset: Herbarium-19 19 (Tan et al., 2019). Formally, each dataset is partitioned into base and novel subsets. The novel subset data is entirely unlabeled, while half of the base data is la- beled during training, with the remaining half left unlabeled. For a fair comparison, we adopt the same random seed in the data split with (Vaze et al., 2022b).
Adopted by (Zhang al., 2024), the class-wise expected distribution m for category is is defined as:
N Σ
m& EE P;
P P
(7)
where N is the batch size, and m has dimension of K x K. For the main branch, is the number of all classes, while Krax denotes the number of base classes in the auxil- iary branch. Intuitively, the k-th probability of my, denoted as mkk,& k). reflects the confidence that "the mini-batch con- tains least one sample belonging category k."
Theorem 3.1. The sum of all elements in m& equals 1, i.e., 1 m+ 1 (Zhang et al., 2024).
Evaluation metric. We adopt cluster accuracy (ACC) to evaluate the performance of our method. More specifically, given the samples' prediction ÿ and ground-truth labels the Hungarian optimal assignment algorithm (Kuhn, 1955) allocates the clustering result and calculates the accuracy. ACC D. Ei""! (μ: = G (i:)), where G denotes the optimal permutation function.
Theorem 3.1 shows that my conforms to the standard prob- ability distribution. Intuitively, the class-wise prediction should be consistent between the two views of the images and close to the one-hot distribution. To this end, for class k. the CDR loss is formulated as
(()) = 1 - (ma; mb)
(8)
where (, denotes the inner product calculation, represent- ing the similarity between two distributions, and m4, is the expectation from another view. Since each class is treated equally, CDR effectively alleviates the bias towards the base classes in the main branch.
Implementation details. In aligmment with prevailing GCD methods, we conduct our experiments using the pre-trained DINO (Caron et al., 2021) ViT-B/16 and DINOv2 (Oquab et al., 2024) ViT-B/14 backbones. Unless otherwise speci- fied, we adopt ViT-B/16 for experimental analysis. Training parameters involve the last block and the auxiliary token across all datasets. The final output retains the features from the CLS and AUX tokens for classification. The default learning rate is set to 0. following cosine annealing decay schedule. Our model trained for 200 epochs with a batch size of 128. Following (Wen et al., 2023), the temperature scalars are T. = 0.1, Ts 0.07, while 73 scales from 0.07
Theorem 3.2. CDDR equals to zero + my equals m' and is a one-hot distribution.
Proof. Please refer to the Appendix A.
Theorem 3.2 indicates that CDR essentially increases the prediction confidence, approaching the one-hot distribution.
5

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GCD via reciprocal learning and class-wisc distribution regularization
Table I. Comparative results on the Semantic Shift Benchmark and Herbarium- 19.
CUB200
Stanford Cars
FGVC-Aircrat
Herbarium 19
Method
Backbone
All
Base
Novel
All
Base
Novel
All
Base
Novel
All
Base
Novel
Avg.
J-means (Macqueen, 1967)
DINO
34.3
38.9
32.1
12.8
10.6
13.8
16.0
14.4
16.8
13.0
12.2
13.4
19.0
RS+ (Han et al., 2021)
DINO
33.3
51.6
24.2
28.3
61.8
12.1
26.9
36.4
22.2
27.9
55.8
12.8
29.1
UNO+ (Fini 2021)
DINO
35.1
49.0
28.1
35.5
70.5
18.6
40.3
56.4
32.2
28.3
53.7
14.7
34.8
ORCA (Cav al., 2022)
DINO
35.3
45.6
30.2
23.5
50.1
10.7
22.0
31.8
17.1
20,9
30.9
15.5
25.4
sCRNCD (Gu al., 2023)
DINO
62.7
71.6
58.2
54.1
75.7
43.7
54.4
59.5
51.8
41.3
60.7
30,9
53.1
GCD (Vaze et ul., 2022b)
DINO
51.3
56.6
48.7
39.0
57.6
29.9
45.0
41.1
46,9
35.4
51.0
27.0
42.7
DCCI, (Pu et al., 2023)
DINO
63.5
60.8
64.9
43.1
55.7
36.2
GPC (Zhao et al., 2023a)
DINO
55.4
58.2
53.1
42.8
$9.2
32.8
46.3
42.5
47.9
36.5
$1.7
27.9
45.3
SimGCD (Wen al. 2023)
DINO
60.3
65.6
57.7
53.8
71.9
45.0
54.2
59.1
51.8
44.0
58.0
36.4
53.1
uGCD (Vaze et al., 2024)
DINO
65.7
68.0
64.6
56.5
68.1
50.9
53.8
55.4
53.0
CMS (Choi et al., 2024)
DINO
68.2
76.5
64.0
56.9
76.1
47.6
56.0
63.4
52.3
36.4
54.9
26.4
54.4
InfoSeive (Rastegar et al., 2024)
DINO
69.4
77.9
65.2
55.7
74.8
46.4
56.3
63.7
52.5
41.0
55.4
33.2
55.6
SPTNet (Wang et al., 2024)
DINO
65.8
68.8
65.1
59.0
79.2
49.3
59.3
61.8
58,1
43.4
58.7
35.2
56.9
LegoGCD (Cao ct al., 2024)
DINO
63.8
71.9
59.8
57.3
75.7
48.4
55.0
61.5
51.7
45.1
57.4
38.4
55.3
RLCD (Ours)
DINO
70,0
79.1
65.4
64.9
79.3
58.0
60.6
62.2
59.8
46.4
61,2
38.4
60.5
SimGCD (Wen al., 2023)
DINOv2
74,9
78.5
73.1
71.3
81.6
66.4
63.9
69.9
60.9
58.7
63.8
67.2
uGCD (Vaze et al., 2024)
DINOv2
74.0
75.9
73.1
76.1
91.0
68.9
66.3
68.7
65.1
CiPR (Hio et al., 2024)
DINOv2
78.3
73.4
80.8
66.7
77.0
61.8
59.2
65.0
56.3
RLCD (Ours)
DINOv2
78.7
79.5
78.3
79.5
91.8
73.5
72.6
77.3
70.3
60.2
71,9
54.0
72.8
to 0.04 within 30 epochs, and the balance weight À 0.35. The default hyper-parameters in our method are specified as 0.5, 8 0.5. The augmentation includes Resize, RandomCrop, Random Horizontal Flip, Color Jittering, and Image Normalization. All experiments are conducted on a single NVIDIA GeForce 3090 GPU based on Py PyTorch.
we present the comparison on generic datasets including CIFARI0/100 and ImageNet-100, While RLCD performs par with existing methods on CIFAR1O. it achieves the best results on CIFAR100 and ImageNet- 100. As a result, RLCD attains the highest average accuracy, improving by 0.8% and 1.6% on DINO and DINOv2, respectively. No- tably, our method surpasses the two-stage SPTNet while utilizing fewer parameters during training. Note that the backbone is pre-trained on the extensive generic dataset like ImageNet- 1k (Deng et al., 2009). Leveraging the strong fea- ture representation of pretrained backbones, existing meth- ods perform comparably on datasets such as CIFAR10 and ImageNet-106 Additionally, the abundance of labeled data in these three datasets minimizes discrimination degradation in parametric methods.
4.2. Main Results
We compare our approach with SOTA methods includ- ing clustering- methods: k-means (Macqueen, 1967),
GCD (Vaze et al., 2022b), GPC (Zhao et al., 2023a),
DCCL (Pu et al., 2023), uGCD (Vaze et al., 2024) In-
foSieve (Rastegar et al., 2024), CMS (Choi al., 2024);
parametric-based methods: SimGCD (Wen al., 2023),
SPTNet (Wang et al., 2024), LegoGCD (Cao et al., 2024)
and strong baseline derived from NCD: RS+ (Han et al.,
Comparison of OB performance. As defined in Sec- tion 3.2, OB serves as a metric to evaluate the base class discrimination capability of GCD models. As depicted in Fig. 5, the proposed RLCD method achieves the best OB performance across all seven GCD datasets, with particu- larly significant improvements on fine-grained datasets. The comparison further highlights the great discrimination of our method.
2021), UNO+ (Fini et al., 2021), ORCA (Cao et al., 2022), CRNCD (Gu et al., 2023). The best results are highlighted in bold and denotes reproduced results.
Evaluation fine-grained datasets. Table shows the comparative results on four fine-grained datasets which are more challenging than the generic. Clustering methods demonstrate inadequate performance, falling far behind the parametric methods on average. The proposed RLCD con- sistently outperforms the others across all four datasets and two backbones, with a notable 3.6% average improvement on DINO. Its strong base performance is attributed to reli- able base logits from the auxiliary branch, while its superior novel class accuracy results benefit from the effective regu- larization loss.
4.3. Ablation Study
Effect of different loss components. As previously out- lined, our approach mainly has four loss components: main branch loss (Main), auxiliary branch loss (AUX), cross- branch distillation (Distill), and class-wise distribution regu- larization (CDR). Here we demonstrate their effectiveness on CIFAR 100 and CUB200 datasets. Additionally, we in- troduce the oracle base class accuracy (OB) as an additional
Evaluation on generic datasets. As shown in Table 2,
6

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GCD via reciprocal learning and class-wisc distribution regularization
Table 2. Comparative results generic image recognition datasets.
CIFAR1O
CIFAR100
ImageNet-100
Methods
Backbone
All
Base
Novel
All
Basc
Novel
All
Basc
Novel
Avg.
k-means (Macqueen, 1967)
DINO
83.6
85.7
82.5
52.0
52.2
50.8
72.7
75.5
71.3
69.4 4
RS+ (Han al., 2021)
DINO
46.8
19.2
60.5
58.2
77.6
19.3
37.1
61.6
24.8
47.4
UNO+ (Fini et al., 2021)
DINO
68.6
98.3
53.8
69.5
80.6
47.2
70.3
95.0
57.9
69.5
ORCA (Cao et al., 2022)
DINO
81.8
86.2
79.6
69.0
77.4
52.0
73.5
92.6
63.9
74.8
CRRCC (Gu al.. 2023)
DINO
97.5
96.6
80.3
84.7
71.5
81.4
94.4
74.8
86.2
GCD (Vaze et al., 2022b)
DINO
91.5
97.9
88.2
73.0
76.2
66.5
74.1
89.8
66.3
79.5
DCCL (Pu al.. 2023)
DINO
96.3
96.9
75.3
70.2
80.5
84.0
GPC (Zhao et al., 2023a)
DINO
92.2
98.2
89.1
77.9
85.0
63.0
76.9
94.3
71.0
82.3
SimGCD (Wen al., 2023)
DINO
97.1
95.1
98.1
81.2
77.8
83.0
93.1
86.7
InfoSieve (Rastegar et al., 2024)
DINO
94.8
97.7
93.4
78.3
82.2
70.5
80.5
93.8
73.8
84.5
CiPR (Hao al., 2024)
DINO
97.7
97.5
97.7
81.5
82.4
79.7
80.5
84,9
78.3
86.6
CMS (Choi et al., 2024)
DINO
82.3
85.7
75.5
84.7
95.6
79.2
SPTNet (Wang al., 2024)
DINO
97.3
95.0
98.6
81.3
84.3
75.6
85.4
93.2
81.4
88.0
LegoGCD (Cao et al., 2024)
DINO
97.1
94.3
98.5
81.8
81.4
82.5
86.3
94.5
82.1
88.4
RLCD (Ours)
DINO
97.4
96.4
97.9
83.4
84.2
81.9
86.9
94.2
83.2
89.2
SimGCD (Wen al., 2023)
DINOv2
98.8
96.9
99.7
88.5
89.3
86.9
88.5
96.2
84.6
91.9
CiPR (Hao et al., 2024)
DINOv2
99.0
98.7
99.2
90.3
89.0
93.1
88.2
87.6
88.5
92.5
RLCD (Ours)
DINOv2
99.0
98.9
99.1
91.2
91.2
91.2
92.1
96.2
90.0
94.1
CIFAR10
SimGCD LegoGCo
Table 3. Ablation experiments on different configurations of loss components: Main, AUX, Distill, and CDR. OB denotes Oracle base class accuracy and M represents only main branch.
Herbarium-19
CIFAR100
CUB200
Main AUX
Distill
CDR
94
Basc
Novel
80
(a)
4
62.1
70.8
$7.7
83.9
94.5
06.0
(b)
7
M
65.7
71.1
63.0
83.7
74.
960
(c)
/
64.3
73.7
59.7
85.9
78.
02
ImageNet-100
(d)
V
66.6
76.4
61.7
86.4
M
69.5
78.1
65.2
86.7
84
/
70.0
79.1
65.4
86.9
86
(g)
4
67.6
71.7
65.6
85.8
(h)
7
M
65.9
73.8
61.9
84.6
Stanford Cars
CUB200
Figure Comparison of oracle base accuracy among SimGCD, LegoGCD, and our RLCDD.
Building on (d), applying CDR to the main branch in (e) improves novel class performance, while applying it to both branches in (f) slightly enhances base class discrimination. yielding the best overall results. It turns out that CDR helps improve base class performance without exacerbating the base class learning bias. When either the Distill (g) the AUX (h) is exclusively removed, our model suffers signifi- cant drop in base performance. This outcome highlights the necessity of both components in our approach.
metric for evaluation.
Table shows the GCD performance with different loss configurations. Specifically, using only the Main loss in (a) serves as the baseline, while (f) represents our complete method. The comparison (a) with (b) indicates that CDR enhances overall performance with minimal influence on OB, contributing substantial 5.3% novel class improve- ment on CUB200. The addition of the AUX loss in (c) leads to comprehensive performance gains and improved discrimination. Since the final block is shared between the two branches, the model learns more robust parameters to fit different tasks, resulting in enhanced feature representation. Comparing (c) with (d) validates that distillation further strengthens performance, improving base accuracy by 2.2%.
sensitivity analysis. As indicated in Equations (6) and (9), we utilize a and 3 control the distillation and regularization strength. Fig. 6 (a) illustrates the GCD performance curves with varying values of a. As a increases, we observe significant improvement in base class accuracy, aligning with the intuition that stronger dis- tillation enhances base class dissemination. However, when a becomes excessively large, it leads to degradation novel
Hyper-paranne

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GCD via reciprocal learning and class-wisc distribution regularization
30
7811
All
LERM shows marginal effects as its performance remains close the baseline. While PDR provides benefits both datasets, the improvements are limited and exacerbate the performance gap between base and novel classes. In con- trast, our proposed CDR effectively boosts novel class ac- curacy while maintaining base performance. Overall, the comparison demonstrates our proposed CDR is more appro- priate for GCD tasks.
Eoom
75
14.2
Accunayy
70-
Accuncy
70-
10.5
64.6
08.3
65.0
625
19.4
65.4
66.1
0
0.2
11.5
0.8
100
0
0.2
0.5
0.8
1.0
(x)
(b)
4.4. Further Analysis
Figure 6. Rffect of different weights of a and B on CUB200.
AUX branch is a good teacher. We conduct a compre- hensive analysis of base-class pseudo-label accuracy across all datasets, comparing our auxiliary branch (AUX), main branch (CLS), and the SimGCD baseline. The dataset names are the abbreviations according to all seven GCD datasets.
class performance, ultimately harming overall accuracy, As shown in Fig. 6 (b), we see that increasing the weight of B notably impacts novel class performance. However, overly large 3 shows negative effect base class accuracy. Our analysis indicates that the optimal values for a and |3 are ap- proximately 0.5, which yields the best overall performance.
Table 4. Comparison of base-class pscudo-label accuracy.
Effect of different regularization. We here present the GCD performance across different probability regularized methods. The baseline is our reciprocal learning frame- work (None) along with the comparative methods, including entropy minimization(ENT) (Grandvalet & Bengio, 2004), minimum class confusion (MCC) (Jin et al., 2020), and label-cncoding risk minimization (LERM) (Zhang et al., 2024). Besides, we modify the CDR loss into pair-wise distribution regularization (PDR) that directly maximizes the probability similarity between two views of a sample.
C10
C100
IN100
CUB
Scars
Aireraft
Her19
SimGCD RLCD (CI.S
98.4
83.6
95.4
80.5
80.7
72.8
68.6
98.4
86.3
95.6
86.9
75.5
76.3
RLCD (XUX)
98.6
87.1
96.2
88.3
91.4
75.9
76.4
From Table 4, the auxiliary branch shows the best pseudo- label accuracy across all datasets. The superior performance of AUX can be attributed to its specialized focus on base class discrimination, which leads to more reliable pseudo- labels. Consequently, this enhanced reliability directly con- tributes to the main branch's ability to maintain strong base class performance during training.
None
ENT
MCC
LERM
PDR
CDR
CIFAR100
80-
Performance under estimated category number. As the previous evaluation is built on the known category num- bers K, we here report the results with estimated categories borrowed from off-the-shelff methods GCD (Vaze et al., 2022b) and GPC (Zhao et al., 2023a). As shown in Table 5, our method demonstrates slightly reduced performance on ImageNet- 100, CUB200, and Stanford Cars when using GCD's 's estimation, yet still outperforms other methods. This performance drop can be attributed the overestimation of K, which leads to unlabeled samples being clustered into larger number of clusters. Notably, the impact CUB200 is minimal, with only a 1.6% degradation. By leveraging the advanced estimation algorithm from GPC, which exhibits more accurate estimation, the performance gap is signifi- cantly reduced across all datasets. The differences are only 0.6%, 0.4%, and 2.5% compared to the ground -oothh refer- ence. The result indicates that approach is not reliant on exact category numbers.
16.2
Accuracy
70-
60-
50
80
CUB200
75.9
26.4
75-
Accuracc (1%)
70-
66.5
67.3
$5.A
65.4
61.8
60
55
All
Base
Novel
Figure 7. Comparison of various regularization methods on CI- FAR 100 and CUB200, where CDR achieves the best results.
Fig. 7 indicates that prevailing regularization methods are not competitive in the GCD task. ENT shows serious nega- tive effect, leading to considerable degradation in both base and novel classes. MCC effectively improves the base per- formance yet harms the novel performance in both datasets.
Effect of varying ratios of labeled samples. In the default experiment, 50% samples are labeled following the main- stream GCD setting. Here we explore the effect of various ratios of labeled samples on model performance.
8

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GCD via reciprocal learning and class-wisc distribution regularization
Table 5. Comparison of estimated category numbers ImageNet-100, CUR200, and Stanford Cars.
ImageNet- 100
CUB200
Stanford Cars
Methods
K
All
Base
Novel
All
Basc
Novel
All
Basc
Novel
RLCD (Ours)
/ 196
86.9
94.2
83.2
70.0
79.1
65.4
64.9
79.3
58.0
GCD (Vaze et al., 2022b)
73.8
92.1
64.6
49.2
56.2
46.3
36.3
56.6
25,9
SimGCD (Wen al., 2023)
81.1
90.9
76.1
61.0
66.0
58.6
49.1
65.1
41.3
109 / 231 / 230
SPTNet (Wang et al,, 2024)
83.4
91.8
74.6
65.2
71.0
62.3
RLCD (Ours)
84.4
93.2
80.0
68.4
77.1
64.1
58.6
76.4
50.8
GPC (Zhao et al., 2023a)
75.3
93.4
66.7
52.0
55.5
47.5
38.2
58.9
27.4
103 212 201
RLCD (Ours)
86.3
94.1
82.4
69.6
78.3
65.2
62.4
78.1
54.8
is expected, given the increased difficulty of clustering with more novel categories. Meanwhile, the strong results on base classes are largely maintained due to the reduced num- ber of base categories. These results highlight the robustness of our approach under varying class distributions.
Table 6. Performance comparison of RLCD and SimGCD under varying labeled sample proportions on CUB200.
Label Ratio (%)
All
Base
Novel
SimGCD
60.3
65.6
57.7
50
RLCD (Ours)
70.0
79.1
65.4
SimGCD
51.0
52.8
49.7
5. Conclusion
25
RLCD (Ours)
63.8
67.5
61.1
In this paper, we propose novel approach for promoting generalized category discovery performance. To enhance base class discrimination in parametric clustering, we in- troduce the Reciprocal Learning Framework (RLF), which consists of two collaborative branches: an auxiliary branch that provides reliable soft labels, and main branch that filters pseudo-base samples using all-class classifier. Ad- ditionally, to mitigate the learning bias towards base classes, we further present Class-wise Distribution Regularization (CDR), which significantly boosts the prediction confidence of unlabeled data and strengthens the discovery of novel classes. The two components are complementary and to- gether constitute RLCD method. Extensive experiments confirm the effectiveness of RLCD, achieving great perfor- mance on both base and novel classes.
SimGCD
34.6
31.8
37.3
10
RLCD (Ours)
46.7
45.3
48.0
Table 6 reveals clear performance degradation pattern as the labeled data ratio decreases. However, our RLCD maintains a significant performance advantage even with severely limited labeled data. The performance gap between RLCD and SimGCD widens in the low-resource scenario, demonstrating our method's superior capability in leverag- ing limited supervision.
Effect of varying novel class numbers. By default, 50% categories are selected as the novel class, following the standard GCD experiment setting. Here, we investigate the impact of varying the proportion of novel classes on model effectiveness, using SimGCD as the baseline for comparison.
A Acknowledgment
This project supported by the National Natural Science Foundation of China (No. 62406192), Opening Project of the State Key Laboratory of General Artificial Intelligence (No. SKLAGI2020P12)) Tencent WeChat Rhino-Bin Fo- cused Research Program, and Doubao LLM Fund. The au- thors also thank Beijing PARATERA Tech CO., Ltd. (parat- cra.com) for HPC support.
Table Performance comparison RLCD and SimGCD with different novel class proportions on CUB200.
Novel Ratio(%)
All
Base
Novel
SimGCD
60.3
65.6
57.7
50
RLCD (Ours)
70.0
79.1
65.4
SimGCD
56.2
65.1
53.3
60
RL.CD (Ours)
62.5
75.6
58.1
Impact Statement
SimGCD
51.8
68.3
49.1
75
RL.CD (Ours)
56.8
78.1
53.2
This paper presents work whose goal is to advance the field of Generalized Category Discovery. There are many poten- tial societal consequences of our work. none of which we feel must be specifically highlighted here.
Seen from Table 7, our method consistently outperforms SimGCD across all metrics as the proportion of novel classes increases. The narrowing gap novel class results

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GCD via reciprocal learning and class-wisc distribution regularization
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Zhu, F., Cheng, Z., Zhang, X.-Y., and Liu, C.-L. Openmix: Exploring outlier samples for misclassification detection. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 12083, 2023.
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GCD via reciprocal learning and class-wisc distribution regularization
Appendix
A. Theoretical Support
Proof of Theorem 3.1
Proof. 1 mg
1 Eisi P.
(8) EN Pi p.)
-
EN P. (1T
Σx 21-1
Σi= p.
Eisi Pi
Proof of Theorem 3.2
Proof. Let a and b be two probability distribution vectors in R to present my and m
[01 .., anl, [b1. b2. bn]
subject the constraints:
Σ
=1 1,
Σ 1=1
b 1,
as 2 0,
by 2 0 for = n.
1=1
The inner product is given by:
a b =
Σ
abb.
i=1
By the Cauchy-Schward Schhar- inequality, have:
2
12
12
n2
Σ asbi
S
Σ
ap
Σ
b
i=1
i=1
Since Σm= a, and Ei= b, 1, we can observe:
n a1
Σ 0
1, Σ 63
< Σ
b.
1.
i=1
Thus, we have:
Σ abbs
-111=1
Cab
i=1
For equality E a,b; = to hold, the Cauchy-Schware inequality must achieve equality, which occurs if and only if a, and b; are linearly dependent:
0₁ = cb; for some constant e all i.
Given the constraints E a; and Σ 2i=1 b; 1, it follows that:
=
b = c - c=
i=1
Therefore, we have:
a; by for all
Besides,
Σ a
0 = a E {0.1}
1=1
which means:
0j for some j and a; = for i #
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GCD via reciprocal learning and class-wisc distribution regularization
Thus, conclude that:
and both are one-hot distributions.
B. Dataset Split
As shown in Fig. 8, we illustrate the datasct split of Generalized Category Discovery(GCD) and compare it with Semi- Supervised Learning (SSL) and Novel Class Discovery (NCD). SSL assumes the labeled and unlabeled data share the same classes, NCD suggests unlabeled data all form the novel classes, while GCD allows the unlabeled data to belong to all classes. The comparison indicates GCD task is more challenging and practical real-world scenarios.
Seml-Supervised Learning
Novel Class Discovery bird
Generalized Category Discovery bird
bird
dog
ladybug
fish
dog
ladybug
fish
dog
ladybug
fish
Labeled
Unlabeled
Figure 8, Difference dataset split among SSL., NCD, GCD.
C. Loss Analysis
Fig. 9 shows SimGCD retains high supervised cross-entropy (SupCE) loss during training, which indicates the noise labels in SimGCD. contrast, our model achieves a near-zero SupCE loss. Since we introduce an auxiliary branch, it can provide more reliable soft labels the main branch. This effectively eliminates noisy information and enhances discrimination capabilities.
CIFAR100
CUB200
1.0
Ours
SimGCD
Ours
SimGCD
0.9
SupCE Loss
SupGE Loss
0.6
0.5
0.4
0.2
Epoch
Figure 9. Supervised cross-entropy descent loss curves CIFARI00 and CUB200.
14

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GCD via reciprocal learning and class-wisc distribution regularization
D. Quantitative Parameter Analysis
We provide an overview of the parameter quantities parametric models in Table 8, Despite incorporating an additional base classifier in the auxiliary branch, our method excludes the projector, resulting in significant parameter savings. The token's contribution the overall model size is minimal, enabling us utilize the fewest parameters during training. During the evaluation, abandon the base classifier and retain the extra token with the backbone, which has negligible parameter overhead.
Table 8. Parameter quantity statistics among parametric models.
Backhone
Classifier
Projector
Extra
Methods
Total
Name
#Param.
Name
#Param.
Name
#Param.
Name
#Param.
SimGCD
VTT-B/16
85,798.656
All
153,600
MLP
6,295,808
None
0
92,248.06
LegoGCD
VIT-B/16
85,798.656
All
153,600
MLP
6,295,808
None
0
92,248,064
SPTNet
VTT-B/16
85,798.656
All
153,600
MLP 6,295,808
Prompts
105.120
92,353,188
RT.CD (Ours)
VIT-B/16
85,798,656
All+Base
230,400
None
Token
768
86,029,824
E. Comparison with Different Uncertainty Weights.
As depicted in Equation (5), we adopt the maximum probability in the auxiliary branch max (p,"") " denote the uncertainty weight of the pseudo-base samples. Here we make a comparison with different uncertainty weights. Cmax is the prototype associated with the maximum probability. When the uncertainty weight set to O, distillation is excluded, resulting in reduced base accuracy. Conversely, the weight of biases the model towards base classes, impairing novel class performance. Using the maximum cosine similarity for uncertainty yields similar results using the maximum probability. Additionally, the uncertainty weight in the auxiliary branch obtains better performance, suggesting its greater reliability compared to the main branch.
Table 9. Comparison of different uncertainty weights.
CIFAR100
CUB200
Uncertainty weight
All
Base
Novel
All
Base
Novel
0
82.3
83.1
80.7
67.6
71.7
65.6
81.6
85.0
75.0
66.5
78.7
60.4
cos (f (zx) (Cma)
82.0
83.6
78.9
68.6
76.9
64.5
cos (faux (x::). m
82.8
83.4
81.6
69.4
78.7
64.8
max (pb)
82.6
83.8
79.9
68.3
76.6
64.2
max (pg.))
83.4
84.2
81.9
70.0
79.1
65.4
F. Discussion with CRNCD
As CRNCD (Gu al., 2023) and our approach both involve distillation, there are several key differences, listed below.
Different tasks. CRNCD aims to deal with novel class discovery (NCD), where all unlabeled data belong to novel classes. In contrast, our focus is GCD, where unlabeled data comprises both novel and base classes. Due to the intrinsic difference between these two tasks, CRNCD demonstrates unsatisfactory performance in GCD.
Different motivations for using distillation. The distillation in CRNCD aims to improve novel class performance, whereas our distillation is intended to promote base class discrimination.
Different training paradigms. CRNCD adopts two-stage training procedure that first trains a supervised model and then freezes it in the second stage. Contrarily, our framework adopts one-stage training in which the main and auxiliary branches help each other simultancously.
Different distilled data. While CRNCD distills all unlabeled data, our approach focuses on pseudo-base data. Here, pseudo-base refers to predictions belonging to the base classes within the main branch.
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GCD via reciprocal learning and class-wisc distribution regularization
Different distillation weights. CRNCD adopts learnable weight function control the distillation strength. We utilize the maximum auxiliary probability uncertainty-based weight, which provides simpler yet effective mechanism for regulating distillation.
Besides the above statement, we conduct thorough experiment to compare the different distillation strategies. Table 10 shows that distilling across all unlabeled data reduces novel-class performance, novel data increases the likelihood of incorrect base-class prediction. Additionally, the learnable distillation weight in CRNCD performs poorly for GCD, causing significant drop in performance. These results highlight the effectiveness of our proposed design.
Table Comparison different distillation strategies.
CIFAR100
CUB200
Distillation strategy
All
Base
Novel
Base
Novel
Distill on all unlabeled data
81.8
83.8
77.8
66.4
75.4
61.8
Learnable weight
80.9
82.6
77.5
65.0
74.5
60.2
RLCD (Ours)
83.4
84.2
81.9
70.0
79.1
65.4
G. Extended Experiment on Estimated Category Numbers.
Since the estimated category numbers from GCP (Zhao al., 2023a) cover the partial datasets, conduct extra comparisons on other datasets under CMS (Choi al., 2024) estimation. Table 11 shows that our method delivers better performance than CMS.
Table Comparison of estimated category numbers on CIFARI00, FGrC-Aireratt, Herbarium 19 and Stanford Cars datasets.
CIFAR100
FGVC PirC--ireraft
Herbarium
Standford Cars
Methods
K
Base
Novel
Base
Novel
All
Base
Novel
All
Base
Novel
CMS (Choi al., 2024)
97/98/666/152
79.6
72.3
60.6
52.4
37.4
56.5
27.1
51.7
68.9
43.4
(Ours) 97/98/66/1122
71.4
78.5
46.0
H. Qualitative Visualization
As shown in Fig. 10, we utilize t-SNE (Van der Maaten Hinton, 2008) to visualize the feature distribution between DINO, SimGCD, LegoGCD, and our model on the FGVC-Aircraft dataset. The t-SNE results for DINO demonstrate poor clustering performance, primarily due the significant domain gap between the ImageNet and Aircraft datasets, which hinders effective feature learning. Mcanwhile, SimGCD and LegoGCD achieve unsatisfactory feature clustering, It is observed that "Class 3" features of SimGCD spread out in the feature space, while LegoGCD forms two clusters of "Class 3". In contrast, our model reveals distinct clusters corresponding to different categories. The visualization comparison validates the superior feature representation of our model.
(a) DINO
(b) SimGCD
(c) LegoGCD
(d) Ours
Figure 10. I-SNE visualization comparing DINO, SimGCD, LegoGCD, and method the FGVC-Airerafi dataset, with samples randomly selected from 10 classes.
16

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GCD via reciprocal learning and class-wisc distribution regularization
I. Further Analysis on H
By definition, H encourages more diverse predictions in the mini-batch. fact, plays an important role in balancing base and novel class performance in the parametric- parametric-based method. However, H would hurt base class discrimination, resulting in degraded oracle base class accuracy. Here, we conduct a deep analysis of the effect of И utilizing class-wise prediction distribution (CPD). Root Mean Squared Error (RMSE) is quantify the class prediction distribution deviation from the Ground Truth.
CUB200 Base
CUB200 Novel
40
200
Ground Truth
Ground Truth
Baseline w/o H (RMSE 45.87)
Baseline w/o H (RMSE 29.97)
30
# Instance Class
150
#instance CCass
2997
100
20
1499
(a)
0-
4496
(b)
0-
0
20
40
60
80
100
20
40
60
80
100
Sorted Class Index
Sorted Class Index
CUB200 Base
CUB200 Novel
70
40
60
Ground Truth
Ground Truth
Baseline (RMSE = 12.19)
Bascline (RMSE = 3.24)
Ours (RMSE = 8.21))
50
#instance/ Class
30
Ours (RMSE 1.76)
# Instance Class
40
20
30
2997
20
10
1499
10
1016
1480
(c)
1346
(d) 0-
3150
0
20
Sorted Class Index
Sorted Class Index
Figure 11. Class-wise prediction distributions on different methods, Concretely, Root Mean Squared Error (RMSE) to measure the prediction distribution deviation from Ground Truth, and the cumulative number is marked the end of each curve.
As shown in Fig. 11, we compare several methods, including parametric-based baseline with and without H, well as our proposed RLCD. When H is removed, all samples predicted the base class, resulting in significantly large RMSE for both base and novel CPDs, 45.87 and 29.97, respectively. From Fig. 11 (c) and (d), we observe that H effectively refines the CPD, reducing the RMSE by 42.63 for base classes and 17.78 for novel classes. However, H also introduces the side effect of misclassifying some base class samples. Specifically, the predicted base class samples are much lower than the ground truth, dropping from 1499 to 1346, which introduces noisy label learning during training. To mitigate this noisy learning, we propose a reciprocal learning framework, where the auxiliary branch provides more reliable pseudo labels to the main branch. Through cross-branch distillation, our method increases the number of predicted base class samples from 1346 1480. Furthermore, our CPD is closer to the ground truth, outperforming the baseline with RMSE reductions of 1.48 and 3.98 for the base and novel classes, respectively.
17

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GCD via reciprocal learning and class-wisc distribution regularization
J. Future Work
Domain generalized category discovery. Extending GCD to handle domain shifts (Wang et al., 2025) between training and testing data remains an open challenge. Future work could explore domain adaptation techniques improve model generalization across different domains while maintaining the ability to discover novel categories.
Continual generalized category discovery. The current GCD setting assumes static set of novel classes. A promising direction is to develop continual learning approaches (Zhao et al., 2023b; 2024) that can incrementally discover and learn new categories over time, while preserving knowledge of previously seen classes.
Generalized category discovery with limited supervision. While current GCD methods require a substantial amount of labeled base class data, real-world scenarios often have very limited labeled data. Future work could investigate transductive few-shot learning approaches reduce the dependency on labeled data while maintaining effective novel class discovery.
18


Reviews:
[{"review_id": "jvkT4LXuhV", "reviewer": "Reviewer_54Bx", "summary": "This paper studies the task of Generalized Category Discovery (GCD). It builds upon parametric-based GCD methods, and proposes a Reciprocal Learning Framework (RLF) that introduces an auxiliary branch devoted to base classification. Within the framework, the main branch filters the pseudo-base samples to the auxiliary branch while the auxiliary branch provides more reliable soft labels for the main branch, leading to a virtuous cycle. The paper further incorporates Class-wise Distribution Regularization (CDR) to mitigate the leaning bias towards base classes. Experiments validate the superiority of the proposed method.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1.** Could the authors explain how the auxiliary helps the main branch intuitively, as well as the effect of Class-wise Distribution Regularization?\n\n**A1.** Thanks for your valuable feedback. \nThe auxiliary branch supports the main branch in two key ways:\n\n-  **Improve Base Performance:** The auxiliary branch provides more reliable pseudo labels to guide the main branch in maintaining high base class accuracy. \n\n-  **Enhance Feature Learning:** Since both branches share the final feature extraction block but are assigned complementary tasks (base-only vs. all-class classification). Consequently, the model is encouraged to learn more robust parameters and develop better feature representations. \n\nClass-wise Distribution Regularization (CDR) serves two key purposes:\n\n- **Mitigate Prediction Bias:** Through class-wise regularization, CDR treats each class equally. Consequently, CDR reduces prediction bias toward base classes, enabling more novel class samples to be correctly classified as novel.\n\n-  **Boost Novel Performance:** CDR implicitly enhances prediction confidence, allowing the novel classifier to better match novel samples and significantly improving novel class accuracy.\n\n\n\n---\n**Q2.** Could the method estimate the number of new classes instead of borrowing off-the-shelf results?\n\n**A2.** Thank you for this constructive comment.  The category number can be estimated using machine learning algorithms such as semi-supervised k-means and agglomerative clustering. Beyond directly adopting off-the-shelf estimates, we introduce a multi-stage refinement process that leverages our improved feature representations to **enhance estimation** precision:\n\n- **Initial Estimation:** We begin with an off-the-shelf method, such as semi-supervised k-means, to establish a baseline estimate.\n- **Refinement:** After training, we re-estimate the number of classes using the enhanced feature representations from our model.\n- **Retraining:** The refined estimation is then used to retrain the model for improved performance.\n\nWe validate this approach on ImageNet-100, CUB200, and SCars, with results shown below:\n\n|  | ImageNet-100 (K: 100) |  |  | CUB200 (K: 200) |  |  | SCars (K: 196) |  |  |\n| --- | ---: | --- | :--- | ---: | --- | :--- | ---: | --- | :--- |\n|  | All | Base | Novel | All | Base | Novel | All | Base | Novel |\n| Est. K (1st) 109/231/230 | 84.4 | 93.2 | 80.0 | 68.4 | 77.1 | 64.1 | 58.6 | 76.4 | 50.8 |\n| Est. K (2nd) 106/218/212 | **85.3** | **93.1** | **81.4** | **69.2** | **76.3** | **65.7** | **59.9** | **77.6** | **51.3** |\n\nThe table shows that the second estimation **more closely** approximate the ground-truth K, consistently enhancing performance across datasets.\nThe improved estimation stems from our model’s **superior feature representation**, which also manifests in clearer class separation in the t-SNE visualizations (see Appendix A.8). Notably, clustering-based methods like GCD and CMS cannot leverage this multi-stage refinement, as their training excludes parametric classifiers.\n\n**Q3.** Please include more recent papers [R1, R2, R3] in GCD to reflect the potential trends of this field.\n\n**A3.** We appreciate this suggestion and will incorporate these recent papers in our revision to provide a more comprehensive view of the field's development.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "There is no theoretical analysis in this paper.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "This paper proposes to employ both main and auxiliary branch, which ensures the basic class learning. This idea is novel to the community of GCD.", "essential_references_not_discussed": "Please include more recent papers [R1,R2,R3] in GCD to reflect the potential trends of this field.\n\nReferences:\n[R1]. Active Generalized Category Discovery. CVPR 2024.\n[R2]. Federated Generalized Category Discovery. CVPR 2024.\n[R3]. Happy: A Debiased Learning Framework for Continual Generalized Category Discovery.", "other_strengths_and_weaknesses": "Strength:\n1. This paper is well-motivated and easy to follow.\n2. The proposed auxiliary learning with additional learnable tokens ensures the learning of the basic classes, which fundamentally improves the overall performance.\n3. The performance gain is remarkable compared with prior arts.\n\n\nWeaknesses:\n1. Could the authors explain how the auxiliary helps the main branch intuitively, as well as the effect of Class-wise Distribution Regularization?\n2. Could the method estimate the number of new classes instead of borrowing off-the-shelf results?\n3. Please include more recent papers [R1,R2,R3] in GCD to reflect the potential trends of this field.\n\nReferences:\n[R1]. Active Generalized Category Discovery. CVPR 2024.\n[R2]. Federated Generalized Category Discovery. CVPR 2024.\n[R3]. Happy: A Debiased Learning Framework for Continual Generalized Category Discovery.", "other_comments_or_suggestions": "Please consider making the dicussion of related work more comprehensive, as in Weakness 3.", "questions_for_authors": "No.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JxOHCpZmIn", "reviewer": "Reviewer_2AkZ", "summary": "The paper proposes a novel approach for Generalized Category Discovery by introducing a Reciprocal Learning Framework and Class-wise Distribution Regularization. RLF enhances base class discrimination through an auxiliary branch that distills reliable base-class predictions to the main branch, while CDR mitigates learning bias toward base classes by enforcing consistency in class-wise probability distributions. Experiments on GCD benchmarks demonstrate state-of-the-art performance, with significant improvements in both base and novel class accuracy.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1.** There is no analysis of pseudo-label accuracy.\n\n**A1.** Please refer to our response to Reviewer UDum-Q1.\n\n**Q2.** Fig. 6 shows the performance variation of parameters α and β but lacks justification for the chosen values.\n\n**A2.** Parameters α and β control the strength of distillation and regularization, respectively. Since the supervised loss weight λ=0.35 in the baseline (SimGCD), we explore values in the range [0,1] to match a comparable scale.\n\n**Q3.** The impact of removing InfoNCE (Sec. 3.1) is not isolated.\n\n**A3.** We have conducted additional experiments to isolate the impact of InfoNCE:\n\n|  | CIFAR100 |  |  | CUB200 |  |  |\n| --- | ---: | --- | :--- | ---: | --- | :--- | \n|  | All | Base | Novel | All | Base | Novel | \n| RLCD + InfoNCE | 82.8 | 83.4 | 81.6 | 68.9 | 76.7 | 65.0 |\n| RLCD | **83.4** | **84.2** | **81.9** | **70.0** | **79.1** | **65.4** | \n\nRemoving InfoNCE consistently improves performance across all metrics. This is because InfoNCE tends to push apart same-class features, which impairs feature discrimination.\n\n---\n\n**Q4.** Table 3 conflates multiple components, making it unclear which contributes most.\n\n**A4.**  Table 3 presents the contribution of each component. The important facts are that RLF benefits base class performance, while CDR significantly improves novel class performance.\n\n**Q5.** RLF is an incremental extension of multi-branch SSL, and CDR resembles distribution alignment in domain adaptation.\n\n**A5.** We would like to highlight several key distinctions:\n\n - **Lightweight Architectural Design:** Unlike traditional multi-branch SSL methods (e.g., MoCo, BYOL) that require separate networks with significant memory and computational overhead, our approach inserts a **single auxiliary token** in the last transformer block. \n\n - **Cross-View Consistency vs. Cross-Domain Alignment:**  CDR is inherently different from domain adaptation methods. CDR operates on **different views** of the same samples to **increase prediction confidence** by enforcing cross-view consistency. This contrasts with domain adaptation, which aims to align distributions across distinct domains.\n\n---\n**Q6.** While performance gains are clear, the method does not address the core challenges of GCD (unknown class counts, domain shift).\n\n**A6.** Our work primarily focuses on improving standard GCD performance, following recent approaches like SimGCD (ICCV 2023) and LegoGCD (CVPR 2024). Regarding the challenges:\n\n-  **Unknown Class Counts**: From our response to Reviewer 54Bx-Q2, our method can refine the estimation through a multi-stage process.\n\n-  **Domain Shift**: While domain shift is a challenge in GCD, methods like HiLo (ICLR2025) address it by leveraging domain adaptation techniques.  We plan to extend our approach to domain-shift scenarios in future work.\n\n\n\n**Q7.** Fig. 1's caption is unclear; \"base logits\" and \"all logits\" need explicit definitions.\n\n**A7.** We will clarify the caption in our revision. \"Base logits\" refers to the output probabilities for base classes only, while \"all logits\" include the output probabilities for both base and novel classes.\n\n---\n**Q8.** How does RLF perform without the shared transformer block? Does freezing the block limit novel-class adaptation?\n\n**A8.** We conducted additional experiments comparing three configurations:\n\n|  | CIFAR100 |  |  | CUB200 |  |  |\n| --- | ---: | --- | :--- | ---: | --- | :--- |\n|  | All | Base | Novel | All | Base | Novel |\n| Shared block | **83.4** | **84.2** | 81.9 | **70.0** | **79.1** | 65.4 |\n| Separate block | 83.1 | 83.7 | **82.2** | 69.5 | 76.6 | **66.0** |\n| Frozen block | 80.5 | 80.7 | 80.2 | 63.3 | 71.3 | 59.4 |\n\nUsing a separate block leads to a **slight performance drop**, as sharing the transformer block allows for learning more robust, task-generalizable parameters. Freezing the block, however, causes a **substantial decline** across all metrics, underscoring the importance of fine-tuning for effective adaptation to both base and novel classes. This aligns with **common practice** in GCD works, where fine-tuning the final block is critical to address domain shifts in downstream datasets.\n\n**Q9.** Table 4 shows performance drops with estimated K. Can CDR be adapted to handle noisy K?\n\n**A9.** Our method inherently handles noisy class number estimates through its multi-stage refinement process, as detailed in our response to Reviewer 54Bx-Q2.\n\n**Q10.** Can Theorem 3.2 be extended to non-ideal cases (when mk≠mk′)?\n\n**A10.**  We would clarify $m_k ≠ m_{k'}$ is **indeed the common scenario**, as these distributions come from different views of batch samples. Theorem 3.2 characterizes the **optimization objective** rather than the **initial condition**.  It aims to maximize distribution similarity between the two views while approximating one-hot. \n\n---\nWe sincerely appreciate the reviewer’s effort in evaluating our paper and hope that our responses adequately address the concerns.", "claims_and_evidence": "RLF improves base discrimination via cross-branch distillation. While ablation studies show gains, the mechanism of reliable soft labels from the auxiliary branch is not validated, and there is no analysis of pseudo-label accuracy or error rates.", "methods_and_evaluation_criteria": "The auxiliary branch design of RLF is intuitive but under-explored and freezing the shared transformer block may limit flexibility. \nBenchmarks are standard, but the ACC metric alone is insufficient. Clustering metrics (NMI, ARI) and robustness to class imbalance are not reported. The assumption of known total class count $ |Y_u| $ is impractical; results with estimated $ K $ (Table 4) show significant performance drops but are not discussed thoroughly.", "theoretical_claims": "Theorem 3.2’s proof (Appendix A.1) correctly uses Cauchy-Schwarz but assumes $ m_k$ and $ m'_k $ are exactly aligned, which is unrealistic in practice. The link between CDR and \"boosting novel performance\" (Sec. 3.3) is not theoretically justified.", "experimental_designs_or_analyses": "Critical parameters ($ \\alpha, \\beta$) are set to 0.5 without sensitivity analysis. Fig. 6 shows performance variation but lacks justification for the chosen values.  \nThe impact of removing InfoNCE (Sec. 3.1) is not isolated. Table 3 conflates multiple components, making it unclear which contributes most.", "relation_to_broader_scientific_literature": "The work builds on parametric GCD (SimGCD, LegoGCD) and SSL but does not discuss connections to *open-world semi-supervised learning* or *multi-task distillation*. The auxiliary branch resembles multi-exit networks, but prior art is unmentioned.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "**Strengths**:  \nPractical design with negligible inference overhead.  \nImproved base-class performance validated across datasets.  \n\n**Weaknesses**:  \nRLF is an incremental extension of multi-branch SSL, and CDR resembles distribution alignment in domain adaptation.  \nWhile performance gains are clear, the method does not address the core challenges of GCD (unknown class counts, domain shift).", "other_comments_or_suggestions": "Fig. 1’s caption is unclear; \"base logits\" and \"all logits\" need explicit definitions.", "questions_for_authors": "1. How does RLF perform *without* the shared transformer block? Does freezing the block limit novel-class adaptation?  \n2. Table 4 shows performance drops with estimated $K $. Can CDR be adapted to handle noisy $K $?  \n3. Can Theorem 3.2 be extended to non-ideal cases (when $ m_k \\neq m'_k $)?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "d1Dt8nxL6Y", "reviewer": "Reviewer_bo6h", "summary": "This manuscript addresses the Generalized Category Discovery (GCD) task, which aims to classify unlabeled data containing both existing (base) and novel (unknown) classes. Existing parametric methods often compromise the discriminability of known classes in order to identify novel classes. To remedy this shortcoming, the authors propose a single-stage Reciprocal Learning Framework (RLF) and a Class-wise Distribution Regularization (CDR) approach.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1.** It lacks a comparison with the latest state-of-the-art method, FlipClass [1].\n\n**A1.** Thank you for highlighting this important work. We observed that FlipClass employs asymmetric augmentation and mixup techniques for performance enhancement from their supplementary materials. For a fair comparison, we incorporated similar advanced augmentation strategies into our approach, resulting in further improvements across datasets:\n\n|  | CIFAR10 | CIFAR100 | ImageNet100 | CUB200 | SCars | Aircraft | Herbarium-19 |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| FlipClass | **98.5** | 85.2 | 86.7 | 71.3 | 63.1 | 59.3 | 46.3 |\n| RLCD | 98.2 | **85.6** | **87.2** | **72.5** | **65.7** | **62.2** | **47.5** |\n\nThe results demonstrate that our method outperforms FlipClass on 6 out of 7 GCD datasets, with particularly significant improvements on fine-grained datasets (SCars: +2.6%, Aircraft: +2.9%).\n\n**Q2.** Because CDR penalizes distribution-level discrepancies between different views, the model tends to produce more balanced class predictions rather than heavily favoring any single class. If one class truly occupies a very small portion of the dataset, the model may naturally lean toward majority classes across several batches. Consequently, CDR might forcibly boost predictions for minority classes, leading to a certain degree of over-correction. In other words, it does not incorporate prior knowledge of strongly skewed class distributions; it merely aims to ensure sufficient agreement and sharpness in class-level predictions from two different views. In the presence of severely long-tailed or extremely imbalanced data, this can introduce errors. The authors do not analyze this scenario in detail.\n\n**A2.** We appreciate this thoughtful analysis of CDR and are pleased to address these concerns with both theoretical insights and empirical results:\n\n- **Implicit adaptive weighting in CDR:** In long-tailed distributions, majority classes typically yield higher-confidence predictions [2], leading to lower assigned probabilities for minority classes. According to class-wise distribution definition, $m_k=\\frac{1}{\\sum_{i=1}^N p_i^{(k)}}\\left(\\sum_{i=1}^N p_i^{(k)} p_i\\right)$, this results in **reduced weighting** of high-confidence majority class samples when computing minority class distributions. Consequently, CDR naturally adjusts sample contributions, establishing an adaptive weighting mechanism that helps **mitigate over-correction**.\n\n- **Balanced learning via CDR and CE**: Our RLCD combines cross-entropy (CE) loss with CDR. While standard CE training in long-tailed settings often biases predictions toward majority classes [2], CDR counteracts this by promoting balanced learning across all classes, **mitigating excessive skew**. This is quantitatively demonstrated in Fig. 11, where our method reduces the Root Mean Square Error (RMSE) by 1.48 and 3.98 for base and novel classes, respectively, compared to the baseline.\n\n-  **Strong performance in the long-tailed setting**: Our RLCD achieves state-of-the-art results on the Herbarium-19 dataset, which features a naturally long-tailed distribution. As reported in Table 1, RLCD surpasses prior methods by **1.3% overall** and **2.5% on base classes**, demonstrating robustness in such scenarios.\n\nFurthermore, we would like to emphasize that RLCD leverages both the Reciprocal Learning Framework (RLF) and CDR. As shown in Tables 1 and 2, the experimental results demonstrate its versatility across **generic**, **fine-grained**, and **long-tailed** scenarios, consistently delivering superior performance.\n\n[1] Lin H, An W, Wang J, et al. Flipped classroom: Aligning teacher attention with student in generalized category discovery. NeurIPS, 2024.\\\n[2] Menon A K, Jayasumana S, Rawat A S, et al. Long-tail learning via logit adjustment. ICLR, 2021.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "No", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "**Strengths**:\n\n​\t1.\tThe manuscript is well-written and logically coherent.\n\n​\t2.\tThe proposed reciprocal learning framework is simple and effective, introducing constraints on old/known classes in a way that enhances performance.\n\n\n\n**Weaknesses**:\n\n​\t1.\tIt lacks a comparison with the latest state-of-the-art method, FlipClass [1].\n\n​\t2.\tBecause CDR penalizes distribution-level discrepancies between different views, the model tends to produce more balanced class predictions rather than heavily favoring any single class. If one class truly occupies a very small portion of the dataset, the model may naturally lean toward majority classes across several batches. Consequently, CDR might forcibly boost predictions for minority classes, leading to a certain degree of over-correction. In other words, it does not incorporate prior knowledge of strongly skewed class distributions; it merely aims to ensure sufficient agreement and sharpness in class-level predictions from two different views. In the presence of severely long-tailed or extremely imbalanced data, this can introduce errors. The authors do not analyze this scenario in detail.", "other_comments_or_suggestions": "None", "questions_for_authors": "Please see weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UZcHY6pVOv", "reviewer": "Reviewer_UDum", "summary": "This paper aims to solve Generalized Category Discovery (GCD). This task seeks to discover both known and novel categories from unlabeled data, leveraging another labeled dataset with only known categories.  Different from previous works that mainly aim to boost model performance on novel categories, this work mainly aims to improve model performance on known categories. To do so, the authors introduce an auxiliary branch with a trainable token to distill known categories for the main branch. Furthermore, the authors introduce Class-wise distribution Regularization (CDR) to mitigate the leaning bias toward known categories. Experiments on wildly-used benchmarks validate the effectiveness of the proposed method.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1:** Experiments about the accuracy of the pseudo labels from the additional branch should be conducted.\n\n**A1:** Thank you for this valuable suggestion. We have conducted a comprehensive analysis of pseudo-label accuracy across all datasets, comparing our auxiliary branch (AUX), main branch (CLS), and the SimGCD baseline:\n\n|  | CIFAR10 | CIFAR100 | ImageNet100 | CUB200 | SCars | Aircraft | Herbarium19 |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| SimGCD | 98.4 | 83.6 | 95.4 | 80.5 | 80.7 | 72.8 | 68.6 |\n| RLCD (CLS) | 98.4 | 86.3 | 95.6 | 86.9 | 90.2 | 75.5 | 76.3 |\n| RLCD (AUX) | **98.6** | **87.1** | **96.2** | **88.3** | **91.4** | **75.9** | **76.4** |\n\nThese results demonstrate that our auxiliary branch consistently produces **more accurate pseudo-labels** across all datasets, with particularly significant improvements on fine-grained datasets (CUB200: +7.8%, SCars: +10.7%).  We will incorporate the analysis into our revision.\n\n**Q2:** What's the difference between the proposed 'base class accuracy' and the 'base' in the result tables.\n\n**A2:** Thank you for the insightful comment. \n\nThe \"oracle base accuracy\" (OB) metric refers to pseudo-label accuracy of base class prediction. OB only considers the base class logits and excludes novel class prediction, providing a direct assessment of the model's discrimination of base classes.\n\nThe \"Base\" column in our results tables reports the standard clustering accuracy on base classes after applying the Hungarian algorithm for label assignment. \n\n\n**Q3:** The performance gain for known categories is not obvious on some benchmarks (e.g., CIFAR-10 and CIFAR-100).\n\n**A3:** Thank you for this insightful observation. The varying performance gains across datasets can be attributed to several factors:\n\n- **Dataset Complexity of CIFAR-10**:  CIFAR-10 is a simple classification task with near-optimal existing results (>97%), leading to a ceiling effect. Its 10 coarse-grained classes also yield high prediction confidence, limiting the impact of our CDR component.\n\n- **Methodological Differences of CIFAR-100**: While our approach falls slightly behind CMS on CIFAR-100 base classes, CMS employs a fundamentally different paradigm (pretraining and clustering) without an all-class parametric classifier. This design choice naturally favors base class performance but limits novel class generalization, where our method demonstrates superior results.\n\nWe would like to highlight that RLCD is designed to achieve balanced performance across both base and novel classes rather than optimizing for base classes alone. The above discussion will be included in our revision.\n\n\n\n**Q4:** What about model performance with fewer labeled samples (e.g., 25% of samples from known categories are labeled)? And what about model performance with more novel categories (e.g., 75% of categories are assumed to be novel)?\n\n**A4:** Thank you for this excellent suggestion. We have conducted extensive experiments varying both the proportion of labeled samples and the ratio of novel categories on the CUB200 dataset:\n\n**Varying Labeled Sample Proportion:**\n\n|  | CUB (50% labeled) |  |  | CUB (25% labeled) |  |  | CUB (10% labeled) |  |  |\n| --- | ---: | --- | :--- | ---: | --- | :--- | ---: | --- | :--- |\n|  | All | Base | Novel | All | Base | Novel | All | Base | Novel |\n| SimGCD | 60.3 | 65.6 | 57.7 | 51.0 | 52.8 | 49.7 | 34.6 | 31.8 | 37.3 |\n| RLCD | **70.0** | **79.1** | **65.4** | **63.8** | **67.5** | **61.1** | **46.7** | **45.3** | **48.0** |\n| Improvement | +9.7 | +13.5 | +7.7 | +12.8 | +14.7 | +11.4 | +12.1 | +13.5 | +10.7 |\n\n**Varying Novel Category Proportion:**\n\n|  | CUB (50% novel) |  |  | CUB (60% novel) |  |  | CUB (75% novel) |  |  |\n| --- | ---: | --- | :--- | ---: | --- | :--- | ---: | --- | :--- |\n|  | All | Base | Novel | All | Base | Novel | All | Base | Novel |\n| SimGCD | 60.3 | 65.6 | 57.7 | 56.2 | 65.1 | 53.3 | 51.8 | 68.3 | 49.1 |\n| RLCD | **70.0** | **79.1** | **65.4** | **62.5** | **75.6** | **58.1** | **56.8** | **78.1** | **53.2** |\n| Improvement | +9.7 | +13.5 | +7.7 | +6.3 | +10.5 | +4.8 | +5.0 | +9.8 | +4.1 |\n\nThese results reveal several important insights:\n\n- **Label Efficiency**: Reducing labeled data degrades performance, yet our RLCD maintains substantial performance advantages even with severely limited labeled data (10% labeled), demonstrating its superiority in low-resource scenarios.\n\n- **Scalability to Novel Classes**: As the proportion of novel classes increases, our method still outperforms SimGCD across all metrics, though the margin narrows for novel classes. This is expected as the clustering problem becomes more challenging with more novel classes. The strong performance on base classes is sustained primarily due to the limited class number.\n\n\n**Q5:** Missing literatures: FlipClass (NeurIPS 2024), Happy (NeurIPS 2024).\n\n**A5:** Thank you for bringing these works to our attention. We will incorporate the suggested references in our revised manuscript.", "claims_and_evidence": "1. The claim that previous methods struggle with identifying known categories is validated through experiments in Fig. 3.\n2. The proposed method aims to boost model performance on known categories, however, the performance gain for known categories is not obvious on some benchmarks (e.g., cifar-10 and cifar-100). The authors should explain the reason for this phenomenon.", "methods_and_evaluation_criteria": "1. For the method, adding an extra branch to provide additional self-supervised signals for known categories is intuitive. However, the authors should explain how to guarantee the quality of pseudo labels from the additional branch. So experiments about the accuracy of the pseudo labels from the additional branch should be conducted.\n2. For the evaluation, the used benchmarks and evaluation criteria are representative.\n3. What's the difference between the proposed 'base class accuracy' and the 'base' in the result tables?", "theoretical_claims": "Theorem 3.1 is from a previous work and Theorem 3.2 is provided with detailed proof.", "experimental_designs_or_analyses": "The experimental designs and analyses can validate the effectiveness of the proposed method.", "relation_to_broader_scientific_literature": "This paper investigates GCD from a different perspective by investigating model performance on known categories.", "essential_references_not_discussed": "Missing reference:\n\n[1] Flipped classroom: Aligning teacher attention with student in generalized category discovery. NIPS 2024\n[2] Happy: A Debiased Learning Framework for Continual Generalized Category Discovery. NIPS 2024", "other_strengths_and_weaknesses": "The proposed model is novel and the experiments validate the effectiveness of the proposed method.", "other_comments_or_suggestions": "1. I suggest the authors to move Fig.3 foward. It is far away from the claim in Line 062 now.", "questions_for_authors": "1. Experiments now assume half of the samples from known categories are labeled, what about model performance with fewer labeled samples (e.g., 25% of samples from known categories are labeled)? And what about model performance with more novel categories (e.g., 75% of categories are assumed to be novel)?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper initially received mixed reviews: 2 * Weak Reject and 2 * Weak Accept. The authors submitted a rebuttal addressing the reviewers’ concerns and questions. After considering the rebuttal and subsequent discussion, three reviewers were satisfied with the authors’ responses and raised their scores to 1 * Weak Accept and 2 * Accept. Although Reviewer 2AkZ acknowledged that some concerns were addressed, they felt the paper still needed improvement in terms of the motivation and detailed presentation, and thus maintained their original score of Weak Reject.

Upon reviewing the comments from Reviewer 2AkZ and the authors’ rebuttal, the AC found that: 1) Reviewer 2AkZ did not clearly point out their concerns regarding motivation and presentation details in the initial review; 2) and the authors have adequately addressed the questions and concerns that were raised.

The AC also acknowledged that Reviewer 2AkZ continued to express concern after the rebuttal that the proposed method does not directly address certain core challenges of Generalized Category Discovery (GCD), such as unknown class counts and domain shift. However, most existing GCD methods primarily focus on improving the clustering accuracy of unlabeled data, which remains a central objective of the field. Therefore, not explicitly tackling class count estimation or domain shift should not be considered a major limitation, as these aspects represent distinct research directions within the broader GCD framework.

Considering these points, the AC believes that the authors have sufficiently addressed the reviewers' concerns and that the strengths of the work outweigh the remaining minor limitation (raised by Reviewer 2AkZ). Thus, the AC recommends acceptance if room permits in the program. The AC also strongly encourages the authors to incorporate all the clarifications from the rebuttal and to further improve the clarity and presentation in the final version.
Decision: Accept (poster)