You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
Authors: Hanyang Li
Yuheng Jia
Hui LIU
Junhui Hou
Venue: NeurIPS 2025 poster ()
Keywords: Deep Clustering
Clustering
Plug-in
Primary area: general_machine_learning
TL;DR: We propose a general parameter-free self-boosting plug-in to improve the clustering performance of various deep clustering models.

Abstract: Recent deep clustering models have produced impressive clustering performance. However,  a common issue with existing methods is the disparity between global and local feature structures. While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we propose **DCBoost, a parameter-free plug-in** designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive $k$-nearest neighbors-based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision.  Subsequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide network optimization. 
Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance of diverse existing deep clustering models. Notably, our method improves the performance of current state-of-the-art baselines (e.g., ProPos) 
by more than 3\% and amplifies the silhouette coefficient by over $7\times$. 
**Code is available at [https://github.com/l-h-y168/DCBoost](https://github.com/l-h-y168/DCBoost).**

You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
Hanyang Li', Yuheng Jia 12,33, Hui Liu Junhui Hou
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China School of Computing Information Sciences, Saint Francis University, Hong Kong, China 'Department of Computer Science, City University of Hong Kong, Hong Kong, China { [lihanyang, yhjia]@seu wwwaeu.edu.ccm edu.cn, cn, h2liuEsfu. edu. jh ho..ouuciyy. edu hk
Abstract
Recent deep clustering models have produced impressive clustering performance. However, common issue with existing methods the disparity between global and local feature structures, While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we pro- pose DCBoost, parameter-free plug-in designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive k-nearest neighbors- based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision. Sub- sequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide net- work optimization. Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance diverse existing deep clustering models. Notably, our method improves the performance of current sfate-of-the--a baselines (e.g., ProPos) by more than 3% on average and amplifies the silhouette coefficient by over 7 x. Code is available at https: // //g thub. wtw.hh.............e....e.ee..ee www.coommoloeeeeeeeeee wttyyy11//DDBeeeee
Introduction
Deep clustering aims use deep neural networks uncover the intrinsic structure of data by partitioning samples into groups based on their similarity, without relying any class labels. Early methods employed autoencoders [12, 37] to extract data representations, which significantly enhanced clustering performance and helped establish deep clustering as a prominent research field. More recently, the integration of self-supervised learning techniques [11, 4, 9] has further advanced deep clustering, leading to impressive clustering performance [36, 20, 13].
Despite the impressive performance, observe that existing methods do construct a reliable global structure, but all build reliable local pattern. For example, as shown in Fig. 1(a), the intra-class similarity is relatively low, and the inter-class similarity remains noticeable, suggesting
*Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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ProPos
+Ours
CC+0urs
Current ACC
Intra-class Similarity
Inter-class Similarity
ProPos
Intra-class Similarity
Inter-class Similarity
0.3
0.8
0.2
0.1
0.0
0.2
CIFAR-20
Silhouette Coefficlent
K-NN Accuracy
ProPostOurs
Silhouette Coefficient
0344
0.8
i-NN Accuracy
0.6
0.7
0.4
0.6
0.2
0.5
0.0
CIFAR 10
CIFAR-20
0.4
CFAR-10
CIFAR-20
(b)
Figure 1: (a) Quantitative comparisons of the global structure in terms of intra-class similarity, inter-class similarity, and silhouette coefficient, and the local structure terms of k-NN accuracy on CIFAR-10 and CIFAR-20. The original model ProPos and CC show weak global structure but strong local structure. Our method maintains their high local accuracy while significantly improving the global structure and overall clustering performance. (b) T-SNE visualization of clustering without (upper) and with (bottom) applying our method on CIFAR-22 Initially, the existing model exhibits severe overlap between different classes, leading to poor separability. After boosting, class boundaries become significantly clearer and more distinguishable.
that samples within clusters lack compactness, while the separation between clusters is also inade- quate. This observation is further supported by their low silhouette coefficient (SC), reflecting poor class separability, unclear decision boundaries, and entangled feature spaces-indicating a general weakness in capturing global structural information. Furthermore, when examining local structural quality with the k-NN accuracy (i.e., the proportion of a sample's k-nearest neighbors (k-NNs) that share the same label), this metric remains consistently high across different deep clustering models, often matching or exceeding clustering accuracy, which means that the feature space still preserves reliable local pattern where semantically similar samples closely grouped. Additionally, the t-SNE visualization Fig. ((b) shows that while some rough cluster formations are observable, the overall distribution remains entangled, with many clusters overlapping lacking clear margins. However, within each loosely formed region samples with similar features tend to group together locally, even if the overall class boundaries are ambiguous. This observation further reinforces our assertion that while existing models face challenges in capturing global semantics, they tend to preserve dependable local patterns.
Building on the above observations, we propose a parameter-free plug-in that leverages trustworthy local structural cues to guide the learning of global feature structure and accordingly improve the clustering performance. Specifically, we first propose a dynamic strategy to identify high- confidence samples that reflect reliable local structures. These samples are then used to construct diseriminative loss, which enhances intra-class compactness, promotes inter-class separability, and preserves instance-level consistency. As shown Fig. (a), after applying our method, the global structure and clustering performance improve significantly. For ProPos on CIFAR-10, the silhouette coefficient increases from 0.10 0.74, while the clustering accuracy (ACC) improves from 94.4% 96.0%. Extensive experiments on five benchmark datasets demonstrate that our method substantially improves the clustering performance of six existing deep clustering models, i.c. our method improves the average performance of current statc-of-thed theh-- baseline ProPos by more than 3%.
In summary, contributions
follows.
We are the first to observe that many deep clustering methods exhibit poor global structure while retaining trustworthy local structures, revealing important phenomenon previously overlooked.

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Based on this sight, we leverage these reliable local cues to guide the learning of global feature structure and accordingly improve overall clustering performance.
We propose DCBoost, parameter-free plug-in integrating an adaptive k--N filtering to select nigh-confidence samples and a discriminative loss that encourages intra-class compactness and inter-class separation, all without requiring manually tuned hyperparameters.
Extensive experiments on five benchmark datasets and six deep clustering models demonstrate the effectiveness, universality, and zero-cost nature of DCBoost.
2
Related Work
Deep clustering aims to learn data representations through deep neural networks while leveraging these learned features to guide clustering. Early methods [12, 37, 10, 29] utilized autoencoders for feature learning. followed by joint training of features and clustering, forming the earliest paradigm of deep clustering. With the rise of self supervieed learning, contrastive [11, 4, 18. 2] and non-contrastive [9, 5] paradigms have been introduced into deep clustering, giving rise to two main branches: representation-based clustering [35, 32, 13, 39, 23] and chustering-head-baseed clustering [36, 20, 7, 27, 25). Representation- -based methods typically apply classical clustering algorithms (e.g.. k-means) on features to generate pseudo labels, which are then fed back as supervision improve representation learning. In contrast, mustering-heed-basee head to the network, allowing pseudo labels be directly predicted and optimized in an end-to- end fashion. Most existing deep clustering models employ strategies such as contrastive learning [20, 13), mutual information maximization [38]. neighborhood consistency [7, 40] to enhance performance. Additionally, heuristic filtering techniques are introduced to remove noisy pseudo labels [36, 27, 30, 16]. relying on classification confidence or consistency between predictions. Both paradigms leverage self-supervisel learning to produce clustering-friende significantly advanced the performance. Recently, large-scale vision and multimodal models like CLIP have provided new insights for deep clustering such as [1, 22], incorporating external knowledge and improving clustering performance.
methods attach a dedicated classification
representations and have
Despite notable progress, we empirically observe that the global structure of existing models may be unreliable with noisy semantics and weak class separability, while local neighborhoods tend to be more stable and reliable. Motivated by this, we propose dynamic sample selection method extracting nigh-confidence samples via local consistency to guide global structure optimization. Our method is parameter- frrameter--rre and applies to both representation- and clustering-heet -ea-tn-----d--beededded models.
Motivation.
3 Proposed Method
Overview. Given an unlabeled dataset X = {z, ) containing unlabeled samples belong-
Algorithm The proposed algorithu DCBoost
Require: Inpat pre-triieed existing deep clustering model
ing to semantic clusters, deep clustering aims
I: Initialize f.(-) and f((-) with M, and initialize s(-) randomly
2: while Clustering do the outpet of f((x) to assign pseudo-labels labels y
to group these samples into different clusters.
Apply k-meuns for = to N/ do
The proposed DCBoost is a generic plug-and and- play algorithm for boosting existing deep clus- tering models. As illustrated in Fig. 2, we initial- ize DCBoost by adopting any pre-trained deep clustering model as the target network Se (.) and duplicating it construct the online network fo (.), followed by randomly initialized non- linear predictor g(-). We apply two types of weak augmentations (-)- and T+(-) to each sample to generate two different views (xx))) and (z:),, which are encoded into L2- normalized feature vectors 2, and 24 by fo and fe (-). For each training batch B of size nB, DCBoost selects a set of high-confidence samples using an adaptive N--N method (Sec. 3.1, local structure mining), which are further utilized to construct discrimination loss to fine-tune the model (Sec. 3.2, local-guided global refinement). To obtain pseudo-labels for all samples Y = {vi ) we apply k-means clustering on the output of the target network at the end of each training epoch for all samples (i.e., the inference branch of Fig. 2). Algorithm I lists the overall process of our DCBoost.
Randomly sugmeat
Select high-
samples using adaptive 6:NN by Eq. (2)
7: Compute the discriminative loss using Eqs. (3), (6), (6), and (8)
Update e(fol )) and fil with the SGID optimizer and exponential moving average, respectively end for
9:
10:
end while
Apply k-means the output fa(z) final clustering
3

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oi
2
(*t))
7
f.(-
g(-)
Online Network
Luse
MesinglAverage
Lenn + Line
76
fo()
T Trggt Network
A daptive NNNN Filtering
le memanss
f.(-)
Inference
Figure 2: Illustration of our DCBoost framework. During training, the gradient backpropagation of the target network is detached, and the parameters of fr(-) are updated using exponential moving average (EMA) from those of f.(.). The overall discriminative loss contains three terms: positive loss Lpos. negative loss Lneg. and instance consistency loss Lins- The inference branch outputs the pseudo labels of all samples the end of each epoch.
3.1 High-Confidence Sample Selection Leveraging Local Structure via Adaptive k-NN Filtering
As previously discussed, empirical evidence indicates that existing deep clustering models excel at capturing local structure, which be used to refine the global feature space to improve the overall clustering performance. However, it is crucial to acknowledge that not all local structures inherently dependable. Local regions, particularly near cluster boundaries, can harbor noisy or ambiguous data points. Thus, it becomes imperative to discern and utilize only the most reliable samples- those exhibiting clear structural coherence-to to provide stable guidance for training.
To this end, we propose a parameter- free approach to extract high- high-confidence samples by leveraging neighborhood label agreement. Specifically, for each sample's 's feature 2; (i.c., the features obtained through the online and target networks, and Appendix. provides more details), we retrieve its nearest neighbors and validate their pseudo-labels. A sample is deemed high-confidence only if all k: neighbors share the same pseudo-label with 2; y Vy. vij E N₂ (z)), where N₂ (2;) ) is the neighbor set for z.. Conversely, if any neighbor exhibits different pseudo-label, the sample likely resides near a class boundary, and should be excluded from high-confidence sample set. However, determining the value of is a challenging task, as the quality of local structures may vary significantly across datasets, models, and training stages, influenced by the number of underlying classes. A small k will include more samples in the high-confidence set but may reduce the average correctness of the pseudo labels due to looser agreement criteria. Conversely, large k ensures higher label consistency and thus reliability, but may result in fewer selected samples, potentially weakening the overall self-supervision signal. Figs. 3(a) and (b) illustrate this phenomenon on three typical datasets.
To solve the above issue, we propose an adaptive determination method to better control the number and quality of high-confidence samples across different training batches. Specifically, we first define of candidate values for k. denoted IK (k., ki = 8, 1, 1,2...., m).. In all experiments, we set m 50 to ensure a comprehensive search space while maintaining computational efficiency. Then, for each k, compute the corresponding score,, as:
scores, k, x
where n, Σn Ily, = シj. Vz, No, (2:) is the number of selected high-confidence samples, given k. is an indicator function that returns 1 if the condition is met and 0 otherwise. We finally select the candidate yielding the highest score the value of k.
Remark. Geometrically, the score in Eq. 1 can also be interpreted as the area of the shaded rectangle as illustrated in Fig. 3(b). Specifically, the z-axis represents the number of neighbors k, and the

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100
CIFAR.20
High-cnfideroo
Accuracy (%)
Proportion (Nu
STL-10
Imbelarce Ratio
15
80
10
99
CIFAR.20
5
10
15
20
10 15
20
Number neighbors k
Number of neighbors
CIFAR-10 CIFAR-20
(a)
(h)
Figure 3: Impact the value on (a) the true accuracy of high-confidence samples, and (b) the proportion of high-confidence samples, i.e., the ratio of selected high-confidence samples to the total number of samples in a batch under given k. A smaller & may reduce the reliability of the selection, whereas larger k generally improves sample quality but selects fewer samples. (c) Comparison of imbalance ratios between high-confident high-confidence samples selected per class can be imbalanced, without proper constraints, classes with larger sample counts may dominate training and negatively affect performance.
samples and all samples across classes. The number of
y-axis represents the corresponding proportion Rz. The score thus corresponds to the rectangular area surrounded by them. This intuitive perspective helps to understand the trade-off being optimized.
After determining the value of the high-confidence samples set XA will be computed by:
% { E B y/, Vzj NN, (z)).
Our method strikes well trade-off between the quality and quantity of self-supervision signals, which allows the model efficiently exploit abundant self-supervision early, while later focusing on highly confident samples, ultimately improving clustering performance. This trade-off is further demonstrated by the variation of selected & values during training shown in Appendix C, Fig.
3.2 Global Structure Refinement via Pseudo Label-augmented Discriminative Loss
We propose to refine the global feature representation by fine-tuning the model with the following discriminative loss:
L Lpos + Long + Linss
where Lpos, Long. and Lins are positive loss term, negative loss term, and instance consistency loss term, respectively. In each batch, Iopos and Lneg are applied to high-confidence samples to enhance the clustering quality, while Lins is applied to all samples to maintain instance-level consistency.
Positive loss Loos- To enhance intra-class compactness, Lyos brings together high-confidence samples with identical pseudo-labels through the formula:
CB
L Lpos
Σ
w.dyy.
2CB
Here, сB represents the number of distinct classes among high-confidence samples set within a batch B, and X, X,, represents the of samples with pseudo-label in the high-confidence set Xx. The term de = . - 2 2 2 2z = signifies the squared Euclidean distance between the outputs of the online and target networks for samples and j sharing the same pseudo-label Furthermore, as depicted in Fig. 3(c), we notice concentration of high-confidence samples in specific classes, i.c., the dominant class will have much more number of samples than the nondominant ones, leading to the pseudo class imbalance problem, which can potentially bias the fine-tuning process. Hence, we introduce a normalization coefficient w. reduce the impact of class imbalance during optimization:
We 1E 20 ExEX, 2'122
(5)
Specifically, we can be seen as the inverse of the square of the L2 norms of the aggregated features from class c. When a class has more high-confidence samples (thus a larger summed feature norm), its influence the loss is automatically reduced. In this way, the loss naturally compensates for the imbalance the number of high-confidence samples across classes. As a summary, the positive loss in Eq. enforces intra-class compactness on high-confidence samples and introduces the weight
5

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coefficient to balance class-wise contributions, refining the feature space and improving overall clustering performance.
Negative loss Ieng. To increase inter-class separability, we repel en cluster prototypes derived from high-confidence samples in each batch. The proposed inter-class separation loss is formulated as:
Ineg
1 o
(1 + 02)
(-2+ 20 o (a1 # e2)),
(6)
=1= c2=1
where and v² represent the 1.2-normalized prototypes computed from the online networks =" and target networks z", respectively:
Σ ΣxEx, 2² ExEE,
Σ L
2
By minimizing the loss in Eq. 6, our method encourages the inter-class separation through Euclidean lataace-bbssel repulsion between prototypes.
Instance consistency loss Lins- To preserve consistency of an instance in the online network and target network, Lins aligns augmented views of the same instance:
Lins = 1 (5 (T (x)) + ae) - fr (T² (z))]l² where & ~ N (0, T),
(8)
where e represents Gaussian noise sampled from a normal distribution .N (0, define / here the identity matrix, and a controls its intensity, which set to 0.001 in all experiments. We treat the feature-space vicinity of one augmented view positive samples for the other view, assuming they share the same semantics. By minimizing the loss in Eq. 8, out method stabilizes global structure and prevents representation collapse.
4 Experiment
4.1 Experiment Settings
Table I: Summary of datasets.
Datasets, backbones and baselines. We evaluated our method on five widely used benchmark datasets, including CIFAR-10 10, CIFAR-20 [17], STL-10 [6], ImageNet- 10, and ImageNet-Dogs [3], as summarized Table 1. To ensure fair evaluation of our boosting effect, we used the same image size and backbone architectures as the original settings of the respective baseline methods. We integrated DCBoost into six existing models, including three representation-baat three clustering-hese
Datasct
Split
#Samples
#Classcs
CIFAR-10
Train+Test
60,000
CIFAR-20
Train+Teest
60,000
20
STL-10
Train+Test
13,000
10
ImageNet- 10
Train
13,000
10
ImageNet-
Train 19,500
methods, i.e., BYOL [9], CoNR [39], and ProPos [13], and
methods: CC [20]. SCAN [36], and CDC [16]. In addition to improving existing models, we compared our method with other deep clustering models. including NNM [7].
GCC [40]. IDFD [34], TCL [21], TCC [32], SPICE [27], SeCu [31], and DPAC [38] to provide comprehensive evaluation. More implementation details are shown Appendix. A.
4.2 Main Results
Significant and consistent improvement on different deep clustering models. As shown in Table 2, our method consistently improves existing models' performance. Notably, models with lower baseline performance, such as BYOL and CC, benefit significantly from our method, with their performance improving by 3.1% % and 4.6%, respectively, greatly enhancing their competitiveness. Even for the strong-pert peron-peroormng ProPos, our method yields an average improvement of approximately 3.2%. allowing it to achieve sante-of-the-as (SOTA) performance on CIFAR- 100 CIFAR-20, and STL- 10 while closely approaching SOTA on other datasets. Moreover, our method further pushes SOTA performance across different existing models on ImageNet- 10 and ImageNet-Dogg highlighting its robust generalizability and effectiveness. Additionally, we also provide results on the large-scale dataset
(200 classes, 100,000 samples) Appendix C, where our method still achieves an improvement of over 2% compared to ProPos.

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Table 2: Clustering performance (%) comparisons on five datasets. The best result for each method is highlighted in bold, while the overall best result is marked with an underline. Average performance,
standard deviation, and significance analysis are provided in Appendix C, Table 18 and Table 19.
Method
CIFAR-10
CIFAR-20
STL-10
ImageNet-10
ImageNet- Dogs
NMI
ACC
ARI
NMI
ACC
ARI
NMI
ACC
ARI
NMI
ACC
ARI
NMI
ACC
ARI
Average
NNM [7]
74,8
84.3
70.9
48.4
47.7
31.6
69.4
80.8
65.0
GCC [40]
76.4
72.8
47.2
47.2
30.5
78.8
63.1
90.1
82.2
49.0
52.6
36.2
IDFD [34]
71.1
81.5
66.3
42.6
42.5
26.4
641.3
75.6
57.5
89.8
95.4
90.1
54.6
59.1
41.3
63.9
TCL [211
81.9
88.7
78.0
52.9
53.1
35.7
79.9
86.8
75.7
87.5
89.5
83.7
62.3
64.4
51.6
714
TCC [32]
79.0
90.6
73.3
47.9
49.1
31,2
73.2
81.4
68.9
84.8
89.7
82.5
55.4
59.5
41.7
672
SPICIE [27]
85.8
91.7
83.6
58.3
58.4
42.2
86.0
92.9
85.1
90.2
95.9
91.2
62.7
67.5
52.6
76.3
SoCu
55.1
55.2
83.6
69.3
DPAC [38]
87.0
93.4
86.6
51.2
55.5
39.3
86.3
93.4
86.1
92.5
97.0
93.5
66.7
72.6
59.8
77.
CC [20]
76.9
85,2
72.8
47.7
41.7
28.8
73.0
80.0
89.9
82.3
65 4
CC+Ours
82.7
198.2
78.5
53.1
48.9
35.2
74.0
80.8
69.4
86.5
90.7
83,3
67.9
70.6
58.5
71.3++.11
SCAN [36]
82.5
90.3
80.8
54.0
53.1
38.5
83.6
91.4
82.5
93.8
97.6
94.8
71.1
73.7
63.4
76.7
SCAN=Ours
37.0
54.5
91.7
95.2
73.0
77.9++12)
CDC [16]
89.0
94.7
89.1
60.6
61.6
16.3
85.8
93.0
85.5
93.1
97.3
94.11
76.8
79.2
70,2
81.1
CDC+Ours
89.9
95.1
90.0
63.0
62.7
48.4
86.6
93.4
86.4
93.3
97.3
94.2
27.5
79.7
71.4
819(-0.8)
BYOL [9]
78.0
87.5
75.2
53.3
52.3
36.0
'75.4
86.1
71.5
$8.4
94,7
88.9
69.7
72.9
60.9
72.7
58.1
54.7
90.2
79.9
90.8
73.4
77.3(+4.6
CoNR [39]
86.7
93.2
86.1
61.7
59.7
45.0
85.2
92.6
84.6
91.1
96.4
92.2
74.2
80.2
67.6
79.8
CurRaQurs
88.0
94.1
87.9
62.2
60.2
45,9
85.6
92.8
85.0
92.4
74.6
80.7
68.3
80.4(+0.6)
ProPos [13]
88. 1
94.4
88.3
60.7
61.6
44.4
83.1
91.6
82.5
90.0
95.8
91.0
72.7
76.9
66.4
79.2
ProPos4Ours
91.1
96.0
91.6
64.5
63.9
49.2
86.7
93.6
86.6
92.7
97.1
93.7
76.3
79.7
70.7
82.2(43.0)
To further illustrate the effectiveness of our method, we visualized learned representations using t-SNE as shown Fig. 4. Compared to original ProPos, DCBoost produces a more globally well- structured embedding space, characterized by tighter intra-class clustering and increased inter-class separation. This improvement is particularly evident in complex datasets such as CIFAR-22 20 in Fig. 1(b) and ImageNet-Dogee in Fig. 4(d). To quantitatively complement these visual observations, as shown in Fig. 5, For silhouette coefficient, which serves as an indicator of global structural quality, our method significantly boosts the silhouette coefficient across various existing deep clustering models, particularly on ProPos and BYO,, indicating a more coherent global feature structure with improved inter-class separation. Meanwhile, the k-NN accuracy, which measures local consistency, remains high and even shows slight improvement, demonstrating that good local structures are not only preserved but also further refined. These observations highlight the effectiveness of leveraging reliable local structural cues to guide and improve the global structure.
ProPos
Ourrs
ProPos+Ours
(a) CIFAR-10
(b) STL-10
ProPos
ProPostOure
ProPee
ProPos+Oure
(c) ImageNet-10 10
(d) ImageNet-Dogs
Figure 4: T-SNE visualization of ProPos (left) and ProPos+Ours (right) on four datasets. The visualization of CIFAR-22 -0 has been presented on Fig. 1(b).
7

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Orginal Nerrosh
Orignal Pattzh
Ryf Ahr
1:04
CosR
Profat CoM
Piske
CONA
Cole
(a) Silhouette Coefficient
(b) k-NN Accuracy
Figure 5: Influence on silhouette coefficient (global structure) and k--N accuracy (local structure).
Table 3: Ablation study (%) CIFAR-10, CIFAR-20 and STL-10.
Filter
Tine
Expox
Tineg
CIFAR-10
CIFAR-20
STL-10
NMI
ACC
ARI
NMI
ACC
ARI
NMI
ACC
ARI
Average
ProPos
88.1
94.4
88,3
60.7
61.6
44.4
83.1
91.6
82.5
77.2
88.0
94.2
87.9
59.5
57.7
39.6
83.9
90.6
79.8
75.7
90.5
95.6
90.8
62.4
61.8
84.8
92.2
83.9
78.7
x
V
88.3
94,4
88.3
57.1
59.1
42.1
83.6
92.0
83.4
76.5
x
89.0
94,9
89.1
63.1
62.5
47.0
84.3
92.0
83.1
78.3
ProPos+Ours
91.1
91.6
64.5
63.9
86.7
93,6
86.6
$0.4
4.3
Ablation Study
Each loss term contribute the clustering performance improvement. From Table 3, we observe that incorporating all proposed losses while keeping our sample selection mechanism fixed yields the best clustering performance. Removing any individual loss component results in a performance drop. highlighting their complementary effectiveness. Specifically, removing the instance consistency loss Tins eliminates instance-level consistency constraints. The model struggles to preserve meaningful structure, leading collapse in clustering performance, evidenced by obvious ACC degradation on both CIFAR- 10 (96.0% 91.1%) and STL.-10 (93.6% 20.5%) Without the positive loss [poe= intra-class consistency is not enforced. Relying solely on inter-class separation provides minimal training guidance, leading to nearly no performance improvement. The removal of negative loss Lneg still has noticeable impact, and its effect would be even more pronounced without sample selection mechanism, which will be further explored in the corresponding ablation study.
Validation of adaptive h-NN filtering, Furthermore, applying all losses without sample selection significantly degrades average performance (80.4% 78.3%), highlighting the critical importance of selecting high-confidence samples. In the absence of sample selection, the removal of Tmeg leads to partial clustering collapse, causing performance to drop below baseline. contrast, incorporating our sample selection strategy significantly improved average performance (75.7% 78.7%), further validating its effectiveness. As shown in Fig. 6(a), high-confidence samples consistently maintain higher accuracy than the overall dataset throughout training, highlighting their reliability in guiding the clustering process and enhancing performance. Additionally, Figs. 6(b)(C) demonstrate that our method adaptively selects the appropriate i value for different datasets: achieve an ACC of 96.0% compared 95.9% with manually set l = 30, and on CIFAR-20. 20, 63.9% compared to 63.6% with k 10. Beyond achieving performance gains, the key advantage robustness manual selection is sensitive to the chosen k, and inappropriate settings can cause notable performance degradation. In contrast, adaptive strategy yields stable improvements without hyperparameter tuning.
300
3FA--1
ACO
95.2
36
High
96.2
80
CITAR Nigocco CIFAR: 23
ACC
(a)
(b)
(c)
Figure 6: (a) Comparison of accuracy between high-confidence samples selected by our methods and all samples. Impact of the value of & on clustering ACC across (b) CIFAR- 10 and (c) CIFAR-20.

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Class-balanced weight uo in Loo. To relief the imbalance class distribution of the selected high- confidence samples, in Eq. we introduce weighting strategy that assigns each class an equal contribution. We conducted an ablation study with three variants: wo. which disables the weighting mechanism; W1, which applies the weights without enabling gradient flow; and Wours, our complete implementation that incorporates weights into the gradient updates. The results in Table 4 show treat-
Table 4: Impact of class-balanced weighting on clustering performance (%).
that Wo yields the worst performance, ing all high-confidence samples equally ignores class imbalance, causing classes with a larger number of high-coofidence samples to dominate the training process. However, W1 significantly improves performance by ensuring each class contributes equally to the objective, effectively mitigating the imbalance. Finally, Wours achieves slightly better results than Wi, suggesting that the magnitude of intra-class representations through gradient-based learning of the weight modulation further refines cluster compactness and improves structural consistency. These results confirm the effectiveness of our class-balanced weighting strategy. More ablation studies are shown in the Appendix B.
CIFAR-I0
CIFAR-20
Method
NMI
ACC
ARI
NMI
ACC
ARI
ProPos
88.1
94.4
88.3
60.7
61.6
44.4
10p
90.2
95.3
90.2
61.9
61.9
45.7
10
91.0
96.0
64.0
63.8
48.9
Moses
91.1
96.0
91.6
64,5
63.9
49,2
4.4 More Discussions
Comparison between representation-basee and seweeriiing-edd--eee architectures. Among the various models we use, CC [20] incorporates both a clustering head and a representation head. applied it separately to
To assess the impact of our method different network components, representation and clustering head, with results shown in Table 5. Across three datasets, in terms of ACC, the representation shows an increase of (3.0%, 6.1%, 1.0%), while the clustering head improves by (5.0%, 8.0%, 2.4%). The larger gains on representation indicate our method is especially effective in refining representation-basee models, i.e., the representation space retains richer semantic information, enabling our method to enhance the learned features more effectively.
Table 5: Performance (%) gain comparison between clustering head (Clu) and representation (Rep).
CIFAR-10
CIFAR-20
ImageNet-Dogs
Method
NMI
ACC
ARI
NMI
ACC
ARI
NMI
ACC
ARI
CC(Clu)
76.9
85.2
72.8
47.1
42.4
28.4
65.4
69.6
56.0
CC(Clu)+Ours
82.7(15.8)
88.2(13.0)
78.5(15.7)
52.5(15.4)
48.5(16.1) 1)
34.4 34.4(16.0)
67 (7,5))
70.6(11.0)
58.5(12.5)
CC(Rep)
78.5
74.9
50.4
33.2
63.3
CC(RspHOurs
85.9(17.4)
91.3(15.0)
83.4(18.5)
58.8(18.4)
56.9(18.0)
42.5(19.3)
66.2(13.9)
68. (12.4)
55.5(12.8)
Table 6: Universality comparison (%) with CoNR.
Table 7: Cluster performance (%) comparisons on CLIP-based models.
CIFAR-10
CIFAR-20
Method
NMI
NMI
ACC
ARI
CIFAR-10
CIFAR-20
Method
Propos
88.1
94.4
88.3
60.7
61.6
44.4
NMI
ACC
ARI
NMI
ACC
ARI
Propos+CoNR
90.3
95.6
90.6
64.0
63.1
48.4
Propos+Ours
91.1
96.0
91.6
64.5
63.9
49.2
SIC
85.0
92.8
84.9
58.2
57.4
43.7
SIC+Ours
85.3
85.0
59.3
58.4
44.9
CC
78.5
86.3
74.9
50.4
48.9
33.2
CC+CONR
84.8
90.8
82.4
58,4
55.7
41.4
TAC
83.4
92.0
83.5
60.8
61.5
46.2
CC+Ours
85,9
91.3
83.4
58.8
56.9
42.5
TAC+Ours
85.5
93.1
85.6
62.4
62.9
48.4
Comparison with CoNR in terms of universality. CoNR [39] enhances intra-class compactness by leveraging contextual cues and filtering boundary samples, which shares some similarities with our method. Therefore, we integrated CoNR into ProPos [13] and CC [20] to evaluate its universality and compare its effectiveness with our method. The results in Table indicate that although CoNR improves performance, the gains are consistently smaller than those of our method. This is largely due to the fact that CoNR limited use of class-level information, as it mainly enforces local consistency with a small set of positive pairs. In contrast, our method can more efficiently exploit reliable local structural information to guide the refinement of global feature organization, leading to more substantial improvements in clustering performance.

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Table 8: Influence of different sample selection methods on clustering performance($6)
CIFAR 10
CIFAR-22 20
ImageNet- Dogs
Method
NMI
ACC
ART
NMI
ACC
ART
NMI
ACC
ARI
ProPos
88.1
94.4
88.3
60.7
61.6
44,4
72.7
76.9
66.4
ProPos+Ours (MOIT sel.)
90.7
95.8
91.1
64.1
63.2
47.8
73.5
77.1
66.6
ProPos+Ours (SSR sel.)
90.9
95.9
91.3
63.9
63.6
48.3
75.3
79.0
70.1
ProPos+Ours
91.1
64.5
49.2
76.3
79.7
70.7
Extension to CLIP-based deep clustering models. We further applied method to CLIP-based models SIC [1] and TAC [22]. where clustering results are obtained using an image encoder followed by clustering head. We incorporated our method into their frameworks by leveraging the existing branch and fine-tuning the clustering head. We re-implemented experiments on the merged dataset (Train+Test), while keeping other settings consistent with [1, 22]. As shown in Table our method brings consistent gains, confirming its effectiveness even in CLIP-basee deep clustering models.
Comparison with different high-confidencce sample selection methods. To better understand the effect of different sample selection mechanisms, we compare our adaptive k--N approach with two representative noisy-label filtering methods: MOIT [28] and SSR [8]. MOIT detects noisy labels by measuring disagreement between sample's annotated label and the class distribution of its k-nearest neighbors in the global feature space, while SSR relies label- nabel-neighborhood consistency. Both approaches require global k-NN search over the entire dataset with a fixed k, and involve additional hyperparameters such consistency thresholds and confidence scores. In contrast, our method is parameter-free and applies an adaptive k-NN search locally within each mini-batch, thereby avoiding the overhead of global retrieval. We re-implemented MOIT and SSR sample selection strate- gies and applied them on ProPos, replacing only the se- lection module while keeping all other training settings unchanged. As shown in the Table 8, integrating MOIT or SSR filtering into ProPos yields consistent improve- ments over the baseline, confirming the importance of neighborhood-band
Table 9: CIFAR-10 ACC (%) using
MOIT and SSR as complete methods.
Method
ACC
ProPos
94.4
ProPos+MOIT
94.4
ProPos+SSR
92.5
selection. However, our adaptive mini-batch selection further achieves the best performance across all datasets and metrics, without introducing extra hyperparameters or global k-NN com- putations. Additionally, when their selection is applied using their own loss to train the model, performance does not improve as shown More experiments are shown in the Appendix C,
ProPos+Ours
96.0
5 Conclusion
In this paper, we have presented DCBoost, a universal, parameter- free and--ayy method to improve existing deep clustering models. Motivated by the observation that existing methods often fail to learn reliable global structures despite consistent local patterns, DCBoost adaptively selects high-confidence samples via local k-NN consistency to guide intra-class compactness and inter-class separation, leading a better global structure and accordingly higher clustering performance. Exten- sive results show consistent improvements across different deep clustering models and benchmarks. A potential limitation is that our method assumes the number of clusters is known and lacks mechanisms to handle highly imbalanced or non-uniform data distributions, which may reduce its adaptability in complex real-world scenarios. In the future, we plan to scale DCBoost to large-scale datasets, explore more adaptive selection strategies and multimodal extensions overcome its current limitations and further boost clustering performance.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants U24A20322, 62576094 and 624221 18, part by the Hong Kong UGC under grants UGC/FDS 1//03/24, UGC/FDS1 and in part by the Hong Kong Research Grants Council
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under Grant 11219324. This research work was also supported by the Big Data Computing Center of Southeast University.
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Appendix
A More Implementation Details
Dataset, image size and backbone. Following [20, 13, 39, 16], we used the merged training and lesting sets for CIFAR-10 10 and CIFAR- 20, only the training set for ImageNet-10 and ImageNet- Dogs. Since our method improved upon existing models, for STL-10, we no longer required unlabeled samples for pre-training and instead used the merged training and testing set. For all methods except BYOL [9] and CoNR [39], image sizes were set as follows: 32x32 for CIFAR-10 10 and CIFAR-20. 96x96 for STL- and 224x224 for ImageNet- 10 and ImageNet- Dogs, following [13, 16]. ResNet-34 served as the backbone for all these methods. For BYOL and CoNR, slight modifications were made to ensure a fair comparison. Specifically, ResNet-18 18 was used for CIFAR-10 and CIFAR-20, while ImageNet-10 10 adopts 96x96 image size, with all other settings unchanged, following [39]. For all methods on CIFAR-10 and CIFAR-20, we followed [13, 16) by replacing the first convolutional filter (7x7, stride 2) with 3x3 filter (stride 1) and removing the first max-pooling layer to better accommodate the smaller image resolution.
Model adaptation. To integrate various clustering models into our framework, we adopted universal adaptation strategy. Specifically, we retained the entire network originally used for cluster- ing ing-whether it comprises backbone followed by a clustering head projector-ag as the target network f.) and duplicated to form the online network f-(.). Additionally, introduced randomly initialized predictor subsequent to the online network, establishing an asymmetric architecture similar to B YOL [9]. For sample selection, We only use the target network's s features for k-NN retrieval (Siy cos (zf 23), where S is similarity matrix). This is because the predictor followed by the online network is randomly initialized, and may introduce noise or unstable representations early in training. To ensure reliable consistency estimation, we rely solely on the more stable target encoder output in such cases. We first conducted warm-up training using Lins for 10 epochs. This step helped the predictor develop initial feature-capturing capability, mitigating the instability that randomly initialized predictor might introduce and ensuring smooth convergence'.
backbone followed by
Experiment settings. We strictly followed the data augmentation protocols from [13, 391, which applied ResizedCrop, ColorJitter, Grayscale, and HorizontalFlip to all datasets, and additionally applied GaussianBlur specifically to 224x224 images from ImageNet- 10 and ImageNet-Dogs, We adopted the stochastic gradient descent (SGD) optimizer, the base learning rate was 0.05, scaled linearly with the batch size of 256. The learning rates for the predictor were 10x the learning rate of feature models. For the exponential moving average hyperparameter to update the online network, we set it 0.996. All these settings strictly followed [13]. We inserted all the existing models into our model at the 800th epoch to inherit the hyper-parameters at that time and continued to train another 200 epochs.
Evaluation metrics. We employed three commonly used clustering metrics to evaluate performance: Normalized Mutual Information (NMI) [33], Accuracy (ACC) [19], and Adjusted Rand Index (ARI) [14). Higher scores indicate better clustering performance.
Baseline methods. For fair comparison, we strictly followed the experimental settings of [16] to re-implement CC [20]. SCAN [36], and CDC [16]. The predictor subsequent to the online network consists of an MLP with the structure (AD-BN [15]-ReLU [26]-dD), where h=512 was set to match the hidden layer dimension of the projector. The output dimension remained the same as the input dimension of predictor to ensure consistency. And we also rigorously adhered to [13, 39] when re- ce-imllementing BYOL [9], CoNR [39], and ProPos [13]. For other deep clustering models, including NNM [7], GCC [40]. IDFD [34]. TCL [21], TCC [32], SPICE [27], SeCu [31], and DPAC [38], we directly cited the results they reported.
For models already based on BYOL-like architecture, required architecture inherently present. Therefore, our method could be directly applied without architectural changes or additional initialization strategies. We also combine outputs from both the online and target networks for sample selection. because the hybrid approach balances the stability of the target network, and the real time adaptability of the online network.
21

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B More Ablation Study
Robustness to cluster number. Previous experiments assumed prior knowledge of the true number of categories, which is often unavailable in real-world applications. To evaluate the robustness of our method different clustering numbers, we conducted experiments on CIFAR-22 with varying numbers of clusters (c = 10, 20, 30, 40, 50), simulating both underclustering and overclustering scenarios. We applied k-means and evaluate clustering performance under the different predefined c. As shown in the Table 10, both NMI and ARI show clear improvements compared to the baseline clustering setting. This suggests that our method enhances the model's s ability to discover meaningful partitions even when the number of clusters deviates from the ground-truth.
Table 10: Clustering performance (%) CIFAR-20 with varying cluster numbers.
CIFAR-20
Class Number
c
10
c
20
30
40
C
50
Metric
NMI
ART
NMI
ARI
NMI
ARI
NMI
ART
NMT
ARI
ProPos
53.6
32.4
60.7
44.4
59.5
38.2
59.5
36.6
58.8
33.3
ProPos+Ours
56.1
34.4
64.5
49.2
62.1
44.6
62.7
42.3
62.2
38.1
Robustness to pseudo-label generation strategies. During training, pseudo labels are typically obtained by performing k-means clustering of all samples each epoch. For models equipped with a clustering head, another option is to directly generate pseudo labels from the softmax outputs, since the output dimension corresponds to the number of clusters. this study, we explored the impact of different pseudo-labeling strategies on clustering performance. As shown in Table 11 our proposed method consistently improves clustering performance across various pseudo-label generation strategies, demonstrating its general applicability.
Table 11: Influence model performance (%) with different pseudo-labe generation strategies.
CIFAR-20
CIFAR-10
Method
NMI
ACC
ARI
NMI
ACC
ARI
CDC
60.6
61.6
46.3
89.0
94.7
89.1
CDC+Ours (k-means)
63.1
62.6
48.6
89.9
95.1
90.0
CDC+Ours (softmax)
62.6
63.2
48.3
89.9
95.1
90.0
NMI and ARI on difffernt & selecting strategies. As shown in Fig. consistent with the previous trend of ACC in Fig. 6, the k value selected manually varies between different datasets and may degrade cluster performance if set inappropriately, while the proposed adaptive ls selection strategy effectively addresses this issue.
CIFAR-10 NMI
(b) CIFAR-10 ARI
(c) CIFAR-20 NMI
(d) CIFAR-20 ARI
Figure 7: Impact of neighbor number k on NMI and ARI across different datasets.
The dynamic retrieval parameter setting. In our method, different & values are evaluated to select an appropriate k, and here we conduct an ablation study on the efficiency of this procedure. Although multiple candidate l values within [1, m) are considered, the m-nearest neighbors are computed only once per batch (e.g., m 50), and all scores under different à are derived from this single retrieval. The operation performs label consistency checks and determines the favorable k
22

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using efficient matrix operations. As result, with a batch size B. the computational complexity is O(B-m) m)) and the overall training cost significantly affected by the choice of m.
Further experiments on CIFAR-10 show that varying m (e.g., m = 25, 50, 75), using a fixed = 25, even removing the k-NN filtering altogether (i.e., k = 0, using all samples) leads to negligible differences in runtime. Meanwhile, the dynamic strategy consistently improves sample selection while introducing observable overhead (see Table 12).
Table 12: Training time for different searching space.
Method
NMI
ACC
ARI
Time (h:m:s)
k 0
89.1
94.9
89.3
4:22:32
h = 25
91.1
91.6
96.0
4:25:58
25
91.2
91.7
96.1
4:25:51
m = 50
91.1
91.6
96.0
4:29:00
75
91.1
91.6
96.0
4:25:58
C More Experiments
Results on large-scale datasets. We further evaluated our method on the large-scale Tiny-ImageNet dataset, which contains 200 classes and 100,000 training images. We followed settings in [16] with ResNet-34 backbone and image size of 64x64. Due to memory constraints, the batch size cannot be set very large, resulting only a few same-class samples per batch, limiting the effectiveness of our batch-wis wise adaptive k-NN selection. To mitigate this, we adopt queue mechanism to expand the neighborhood search space, following [13], and manually set a fixed k to preserve high- confidence sample selection. Experimental results in Table 13 demonstrate that our method is not only effective on standard datasets but also adaptable to larger-scale datasets.
Table 13: Clustering performance (%) on Tiny-ImageNet.
ImageNet
Method
NMI
ACC
ARI
ProPos
46.5
29.8
18.2
ProPos+Ours
48.9
31.9
19.8
CDC
47.5
33.9
19.9
CDC+Ours
48.0
34.7
20.9
Scalability to datasets with many eategories. To evaluate the scalability of our method scenarios with large number of categories, we further conducted experiments CIFAR- 100 (100 classes), Tiny-ImageNar (200 classes), and Image (1000 classes). As shown in the Table 14, our method consistently improves over the ProPos baseline across all datasets. These results confirm that our method is scalable and effective even as the number clusters increases from tens to hundreds or thousands.
Table 14: Clustering performance(%) on datasets with many categories.
CIFAR-100
Tiny ImageNet
ImageNet-IK
Method
NMI
ACC
ARI
NMI
ACC
ARI
NMI
ACC
ARI
ProPos
65.7
55.2
40.1
46.5
29.8
18.2
51.3
22.0
13.4
ProPos+Ours
67.4
57.1
43.4
48.9
31.9
19,8
54.5
23,2
15.1
Results on long-tailed datasets. To further validate the robustness of our method, we conducted additional experiments on long-tailed CIFAR-10 and CIFAR-20 with an imbalance ratio of 10, where the number of training samples per class follows exponential decay controlled by ratio between
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the number of samples in the most frequent class and the least frequent class. For instance, with ratio=10, the head class contains 5000 samples while the tail class has only 500. As shown in Table 15, our approach consistently improves clustering performance over strong baselines. These gains highlight that the proposed k-NN-based selection strategy remains effective and robust, even under class-imbalanced conditions. Notably, although our method does not explicitly incorporate mechanisms tailored for non-uniform data distributions, it still yields consistent improvements across different datasets and baselines.
Table 15: Clustering performance(%) on long-tailed datasets.
CIFARIO-LT
CIFAR20-LT
Method
NMI
ACC
ARI
NMI
ACC
ARI
ProPos
57.1
48.3
40.7
47.6
42.1
29.9
ProPos+Ours
61.3
49.9
43.3
49.1
43.4
31.5
CoNR
67.2
64.6
56.7
51.2
43.9
30.3
CoNR+Ours
68.4
65.3
59.1
51.4
44.1
33.3
LFSS [24]
57.9
56.2
43.0
46.7
41.4
28.3
LFSS+Ours 61.3
60.1
46.9
47.5
42.4
29.4
Clustering performance curve comparison. To assess the observed performance gains are derived from our proposed method rather than prolonged training, we conducted study comparing the original model with and without our method under extended training epochs. As shown in Fig. simply extending the training time of ProPos does not lead to significant improvement and may even degrade performance. In contrast, incorporating our method results notable and stable enhancement in clustering ACC.
80
ProPos1OUS
MM wommoa oh
(3)
ACC (M)
77.5
27
76.5
50
800
850
900 Epoch
950
1000
850
Epoch
(a) ImageNet- Dogs
(b) CIFAR-20
Figure 8: Evaluating the effect of prolonged training and method.
Training efficiency. We reported the running time per epoch on single RTX3090 GPU in Table 16. Compared to CDC [16] (100 epochs after pretraining). ProPos (200 epochs), and SCAN [36] (300 epochs after pretraining), our method exhibits a comparable per-epoch runtime to ProPos and SCAN, and is significantly faster than CDC. This suggests that our framework introduces negligible computational overhead and maintains high efficiency during training.
Enhancement on CoNR. CoNR [39] can be applied to existing models to boost their performance. However, when our method is further applied on top of
models, we observe additional performance gains in Table 17. This demonstrates that method complements CoNR's ability to improve clustering performance.
Average performance and standard deviation for deep clustering models. To ensure the reliability of our experimental results, we conducted five runs across various models. Table 18 reports the average performance along with standard deviations. For each existing deep clustering model,

---

Table 16: Running time comparison.
Running Time Per Epoch (Minute)
CIFAR-10
CIFAR-20 20
STL-10
ImageNet-10 10
ImageNet Doggs
CDC
5.2
1.4
0.8
3.6
3.5
SCAN ProPos
0.9
0.9
0.3
0.8
1.2
1.1
0.4
1.4
CDC/SCAN+Ours (200 epuchs)
1.1
1.1
0.3
1.4
1.7
ProPos+Ours (200 epochs)
1.1
0.4
1.4
Table 17: Further enhancement (%) after enhancement of CoNR on ProPos and CC.
CIFAR-10
CIFAR-20
Method
NMI
ACC
ARI
NMI
ACC
ARI
ProPos
88.1
94.4
88.3
60.7
61.6
44.4
ProPos+CoNR
90.3
95.6
90.6
64.0
63.1
48.4
ProPos+CoNR+Ours
91.1
96.0
91.5
64.2
63.5
48.8
CC
78.5
86.3
74.9
50.4
48.9
33.2
CC+CONR
84.8
90.8
82.4
58.4
55.7
41.4
CC+CoNR+Ours 86.9
92.1
84.7
58.8
56.0
42.2
integrating our method consistently leads to notable performance gains compared to the original results, all of which statistically significant under 5 /-test, corresponding p-values shown in Table 19.
Table 18: Clustering performance NMI, ACC, ARI (meantstd %) of different deep clustering models on five image benchmarks.
Methol
CIFAR-10
InsageNe
N31
ACC
ASCI
NB41
AEC
ARI
NMI
ACC
A31
1041
ACC
ARI
XMI
NCC
ARI
CCHOus
61.2e2.4
$6.5e3.3
54.3e1.3
$8.5a1.5
3699-1
76.224.3
$5.9550
751e6.
96.908.3
$4.46111
67.36116
09.62.11
57.7e88
SCAN
56 1e2.]
51 $1.4e117
13.9631
53.7216
51 11.92211
37 322
91 S15.5
53 365.
50.62..
91.45377
35.5 $5.9.32511
70.53.6
T3 Gef.6
(2.5x5.8
SCAN-OKE
85.912.1
91.9117
$4.133.2
56-41.9
$3.511.8
21112
92.12144
$4.18.
91.12.7
91.5577
89.115.2
7336A33
74.566.
68.165.6
00:51177
Ra54
BYOL
7676225
56.5e1.1
73:6633
53.5095
51 3
74.5a1
85 05.4
TO 20.2
37.6036
94.3608
18.1
011h33
72 Sehl1
BYOLIOus
84728.5
91.1a3.1
53.1418
5736977
$4941.8
41.0
N1.3228
2035
79345-4
$9.958.1
$5.7.0.1
90.66.
743366
7..6
CuNR:Ous
87.5e1.0
93.6e1.4
68.3
$2.3
$41e1.0
$6.4880
749603
79.514
68.681.3
Philte
58 15.41555
04.5653
60.180.5
600115
413umS
82
911212
38.513
95.010.9
19.119
16.3653
PhiPteeOuuss
50.18.6
55.5183
90.518.7
63.111.1
62.1114
41A111
86.385
88.9687
91.1111
96.6803
92.111
76.1217
7980
30.5885
Table 19: T-test results comparing baseline deep clustering models and their enhanced versions with method across five image benchmarks. A p-value less than 0.05 indicates statistically significant improvement over the baseline.
CIFAR-10
CIFAR-20
ImageNet-10
ImageNettDogs
Method
NMI
ACC
ARI
NMI
ACC
ARI
NM1
ACC
ARI
NMI
ACC
ARI
NM1
ACC
ARI
SCAN-Ours
0.001
0.002
0.005
0.003
0.004
0.000
0.001
0.016
0.000
0.003
0.019
0.018
0.015
0.048
0.023
CDC+Ours
0.001
0.019
0.014
0.000
0.000
0.000
0.002
0.001
0.001
0.037
0.001
0.047
0.000
0.001
0.000
BYOL+OOrs
0.002
0.001
0.001
0.002
0.005
0.000
0.000
0.003
0.044
0.028
0.018
0.020
0.000
0.000
0.000
CorR
0.001
0.002
0.003
0.007
0.021
0.020
0.036
0.044
0.047
0.017
0.032
0.020
0.040
0.032
0.040
ProPus+Ours
0.004
0.037
0.008
0.010
0.001
Visualizations of adaptive i during training. We plot the variation of the adaptively selected
values across training epochs. As shown in Fig. 9, & starts relatively small, enabling the inclusion of more samples and thus stronger self-supervision signals at early stages. As training progresses, gradually increases, reflecting stricter neighborhood consistency criteria and leading the model to focus on more reliable high-confidence samples. This adaptive adjustment verifies the effectiveness of our method in balancing supervision quantity and quality throughout training.
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1.00
0.90
0.96
Adgphe sthic.r
0.94
0.92
0.90
ANN
24
100
850
660
Eposh
Figure 9: Visualizations of adaptive k.
Figure Accuracy change for each class.
Visualizations of accuracy change for each class. To further analyze the effect of our method the class level, we visualize the accuracy change of each category before and after applying DCBoost on CIFAR-10. The corresponding statistics are shown in Fig. 10. Our method consistently improves the accuracy across all 10 classes. These results demonstrate that our selection strategy does not overfit bias toward specific categories, but instead achieves consistent gains across diverse semantic groups. Moreover, the improvements suggest that our method refines the global feature space in class-balanced manner, ensuring that minority or challenging categories also benefit from the enhanced representations.
Visualizations of high-confidence samples. We visualize the feature space with t-SNE in Fig. 11, showing three subfigures: true labels, pseudo-labels, and high-confidence samples (red) versus others (blue). We observe that in regions where pseudo-labels disagree with ground truth (circled in the second subfigure), very few high-confidence samples are selected, meaning our method avoids unreliable areas. In well-formed clusters, high-confidence samples densely distributed, showing that our method can reliably capture consistent regions. These observations indicate that our selection strategy filters out noisy regions and focuses on structurally reliable clusters, which in turn helps refine the global feature structure and improve clustering quality.
tSNE (True-lbbl)
SNE (Psudb-labee label)
t:SNE (High conficencee and others)
Figure 11: Visualizations of high-confidence samples on CIFAR-10.
26


Reviews:
[{"review_id": "tHDwZxd2YO", "reviewer": "Reviewer_EzRr", "summary": "This paper presents a novel deep clustering enhancement method, DCBoost, aimed at improving clustering performance by refining the global structure of existing deep clustering models. DCBoost is a plug-in that does not require manual hyperparameter tuning, and it leverages local structure information to optimize the global feature structure, thereby enhancing clustering accuracy. Specifically, the method employs an adaptive k-nearest neighbor (k-NN) approach to select high-confidence samples, and discriminative loss is computed based on these samples to enhance intra-class compactness and inter-class separability during training. Experimental results show that DCBoost significantly improves the performance of existing deep clustering models across multiple benchmark datasets and demonstrates strong generalization ability across various datasets.", "questions": "1.\tThe description of the dynamic selection mechanism is unclear. It is not specified whether retrieval is performed only on the target network’s output or if both target and online network outputs are considered. More implementation details are needed;\n2.\tIs dynamic retrieval necessary for every batch? If so, does it require saving historical sample representations? The method for updating these representations should also be discussed;\n3.\tThe choice of setting the dynamic retrieval parameter m to 50 needs further justification. Does this lead to excessive computational complexity? A comparison of time consumption, especially with a fixed m, would clarify the computational trade-offs;\n4.\tHow can the method ensure the selection of high-confidence samples from different classes in each batch? It would be helpful if the authors could show how clustering precision changes across different classes before and after applying the method;\n5.\tRegarding the score visualization, the authors explain the score as the area of the shaded rectangle, which is intuitive. However, replacing the x-axis with precision could better illustrate the trade-off between quality and quantity, which warrants further exploration;\n6.\tHow does the proposed method compare with existing high-quality sample selection methods in terms of accuracy and computational efficiency? What is the time complexity of the proposed method compared to others?", "limitations": "The authors have acknowledged the following limitations in the main text.\n“A potential limitation is that our method assumes the number of clusters is known and lacks mechanisms to handle highly imbalanced or non-uniform data distributions, which may reduce its adaptability in complex real-world scenarios.”", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1.\tThe paper proposed a novel adaptive k-NN method for selecting high-confidence samples, effectively addressing the challenge of choosing the optimal k in clustering tasks. It aims to strike an optimal balance between the quality and quantity of self-supervision signals, thus improving clustering performance.\n2.\tThe paper provides extensive comparison and ablation studies, thoroughly validating the effectiveness of the proposed method in improving the performance of existing deep clustering models.\n\nWeaknesses\n\n1.\tCertain parts of the paper, such as the Adaptive k-NN Filtering in Section 3.1, require further clarification to enhance understanding of the method's detailed operation.\n2.\tThe experimental setup, including batch size and total training epochs, is not sufficiently detailed. This information is essential for understanding the reproducibility and scalability of the experiments.\n3.\tSome method implementations, such as those referenced in Table 2 (e.g., CC, ProPos), are unclear. It is not specified whether the proposed method modifies the backbone network or if it requires additional components like a projection layer or clustering head.", "was_revised": true, "final_justification": "My primary concerns have been resolved, and I will update my rating accordingly.", "rebuttal": "We sincerely thank the reviewer for their valuable comments and constructive feedback. We would like to address each of your concerns in detail, and clarify points that may have caused confusion.\n\n**W1&Q1. The description of the dynamic selection mechanism.**\n\nThanks the reviewer for pointing out the need for more clarity regarding our dynamic selection mechanism. We clarify the implementation details as follows:\n\n**For BYOL-like architecture (BYOL, CoNR, ProPos):** We combine outputs from both **the online and target networks.**  The similarity matrix $S$ is computed as: $S_{ij}=\\cos\\left(z^o_i, z^t_j\\right)$ to search the $k$-NN. This hybrid approach balances the stability of the target network, and the real-time adaptability of the online network. By using both, the neighborhood consistency reflects both stable semantic structure and recent representation shifts.\n\n**For other architectures(e.g., CC, SCAN, CDC):** We only use the **target network's features** for $k$-NN retrieval. ($S_{ij}=\\cos\\left(z^t_i, z^t_j\\right)$). This is because the predictor followed by the online network is randomly initialized, and may introduce noise or unstable representations early in training. To ensure reliable consistency estimation, we rely solely on the more stable target encoder output in such cases. \nWe will revise the manuscript accordingly to clearly describe these design choices and implementation details.\n\n\n**W2. Experimental setup about batch size and total training epochs.** \n\nIn fact, we have already provided comprehensive information regarding the experimental setup in **Appendix A**, including backbone architectures, dataset sizes, training durations, batch sizes, and other relevant hyperparameters. For clarity, we would like to reiterate here that our method uses a **batch size of 256** and is trained for **200 epochs** based on the existing models. We believe this level of detail is sufficient to ensure the **reproducibility and scalability** of our experiments. **Besides, we have submitted our code in the supplementary file**, which makes the reproducibility easy.\n\n\n**W3. Whether the proposed method modifies existing models.**  \n\nOur proposed method is applied directly on the existing models without modifying their backbone networks or any other components. **As detailed in Appendix A**, for models already based on BYOL-like architecture such as BYOL, CoNR, and ProPos, **neither the backbone nor additional modules are changed or added** during our enhancement. For other models like CC,SCAN and CDC, we duplicate their clustering network to form online and target networks, and add only **a lightweight MLP predictor after the online network**. Still, the backbone networks and original modules remain unaltered. \n\n\n**Q2. Is dynamic retrieval necessary for every batch?**\n\nYes, our method performs  $k$-NN-based clean sample selection at **each training batch**. That means, **if the batch size is $B$, we compute $k$-NN using only $B$ samples.** By default, we perform **within-batch retrieval**, which is efficient and sufficient for all the results reported in the main paper. Thus, **no historical representations are used in these main experiments.** \n\nInstead, to handle **rare edge cases** (e.g., when a batch lacks sufficient same-class samples), we introduce a **memory bank** of recent features from the **target network**. This bank is continuously updated with the most recent representations at each training step. This mechanism is only used in the extended experiment in Appendix B, and does not involve maintaining long-term historical representations.\n\n\n**Q3. The dynamic retrieval parameter m setting.**\n\nWe clarify that evaluating multiple candidate values of $k$ (from 1 to $m$) **does not require performing $k$-NN retrieval multiple times**. Instead, **we compute the $m$-nearest neighbors only once** per batch (e.g., $m=50$), and **all scores under different $k$ are evaluated based on this single retrieval result.** The function $find optimal-k-balanced$ (see code) performs label consistency checks and computes an favorable $k$ using efficient matrix operations. Therefore, if the number of samples within the batch is $B$, the overall computational complexity of dynamic retrieval remains $O(B·m)$ per batch, so the whole computational complexity of training is not significantly affected by the value of $m$. \n\nWe have also added a new experiment on CIFAR10 varied $m$ (e.g., $m$=25, 50, 75), replaced the dynamic strategy with a fixed $k$=25, and even removed the $k$-NN-based filtering altogether (i.e., $k=0$, using all samples) as follows. **All these variants showed negligible difference in runtime.** This confirms that our strategy introduces **no observable computational overhead**, while effectively improving sample selection.\n|Variant|NMI|ACC|ARI|Training Time (h:m:s)|\n|--|--|--|--|--|\n| ProPos|88.1|94.4|88.3|-|\n|k = 0| 89.1|94.9| 89.3|4:22:32|\n|k = 25|91.1|91.6 |96.0|4:25:58|\n|m = 25|91.2|91.7|96.1|4:25:51|\n|m = 50|91.1|91.6|96.0|4:29:00|\n|m = 75|91.1|91.6|96.0|4:25:58|\n\n\n**Q4. How to ensure the selection of high-confidence samples from different classes?**\n\nTo demonstrate that our method selects high-confidence samples across all classes (rather than biasing toward a few), we have shown how clustering precision and recall change across different classes before and after applying our method on CIFAR-10.\n\n**Precision(%):**\n|Method\\Class|0|1|2|3|4|5|6|7|8|9|\n|---|---|---|---|---|---|---|---|---|---|---|\n|Propos|93.7|97.6|90.7|90.7|94.3|87.1|98.2|95.7|98.8|98.3|\n|Propos + Ours| **95.2**|**97.9**|**97.2**|**92.3**|**95.1**|**90.2**|**98.4**|**96.4**|98.7|**98.5**|\n**Recall(%):**\n\n|Method\\Class|0|1|2|3|4|5|6|7|8|9|\n|---|---|---|---|---|---|---|---|---|---|---|\n|ProPos|97.5|95.6|93.2|87.0|97.3|88.3|98.7|97.0|92.8|97.4|\n|ProPos + Ours|**98.3**|**97.4**|**93.5**|**89.5**|**97.7**|**90.6**|**98.8**|**97.8**|**98.8**|**98.0**|\n\nOur method **improves the precision in 9 classes and recall in all 10 classes**. This confirms that our selection strategy does not favor specific classes, but instead **works consistently across diverse semantic groups.** The base model already yields relatively high clustering accuracy, and the local consistency sample selection is effective at identifying confident samples **within each semantic region** of the feature space. It **naturally achieves balanced coverage** across classes. The results also indicate that our method **helps refine the global feature space in a class-balanced manner.** \nWe will include this analysis and the accompanying tables in the final version of the paper to further clarify this point. \n\n\n**Q5. Illustrating the trade-off through visualization.**\n\nThank you for the constructive suggestion. We chose $k$ as the x-axis because our method adaptively selects the proper $k$ based on label consistency, and **the geometric interpretation provides an intuitive way to compare different $k$ values.** We agree that using precision as the x-axis could offer another interesting perspective to visualize the trade-off between quality and quantity, and we appreciate the suggestion and will add the figure in our final version.\n\n\n**Q6. Comparison with existing high-quality sample selection methods.**\n\nWe have added new experiments comparing our method with several existing sample selection methods SSR[1],MOIT[2] and SCAN[3]. SSR and MOIT are **noise-robust sample filtering methods** and can directly replace our filtering mechanism, and SCAN is another sample selection **in deep clustering**. The evaluation covers clean sample accuracy, the number of true and false clean samples selected, and training time. Note that SCAN uses a confidence threshold to filter samples and is not directly applicable to the ProPos-like representation-based method. For a fair comparison, we treat SCAN as another baseline and evaluate both its original method and our sample selection method.\nAs shown, our method is more conservative, aiming to minimize false positives. It achieves the **lowest number of incorrectly selected clean samples** while maintaining competitive accuracy.\n\n|Method|True Clean / Selected Clean|Accuracy(%)|False Clean Number|\n|---|--|---|---|\n|Propos + SSR|53978 / 55333|97.55|1355|\n|Propos + MOIT|29018 / 29750|97.54|732|\n|Propos + Ours |29720 / 30407|**97.74**|**687**|\n|SCAN |48609 / 49300|98.60|691|\n|SCAN + Ours|28458 / 28768|**98.92**|**310**|\n\nThe computational overhead of sample selection alone is difficult to isolate; thus, we report **end-to-end training time**, including selection and model learning. We directly compare **the third stage of SCAN** and our method, and report **full SCAN runtime (stage 2 + 3)** in Appendix C, Table 11. Due to limited resources, SCAN experiments were run on an **RTX 3090**, while other methods were evaluated on **V100**.\n\n|Method|ACC(%)|Time (hour)|Device|\n|---|---|---|---|\n|ProPos|94.4|-|-|\n|ProPos+MOIT|94.4|-|-|\n|ProPos+SSR|92.5|-|-|\n|ProPos + Ours(MOIT selection)|95.8|5.0|V100|\n|ProPos + Ours(SSR selection)| 95.9|4.8|V100|\n|ProPos + Ours|**96.0**|**4.5**|V100|\n|SCAN|90.3|**3.7**|3090|\n|SCAN + Ours|**90.8**|3.8|3090|\n\nSSR and MOIT require full-dataset evaluation for sample selection, which increases the runtime. In contrast, our method leverages a **$k$-NN-based filter within each training batch**, enabling more efficient selection. Although SSR and MOIT improve performance when plugged into our training framework, such gains depend on our model design and loss functions. Used independently, these methods do not yield comparable performance, emphasizing the importance of integrating selection and training cohesively. Compared with SCAN, our method achieves a comparable efficiency but higher ACC.\n\n[1] Multi-objective interpolation training for robustness to label noise. CVPR, 2021.\n\n[2] Ssr: An efficient and robust framework for learning with unknown label noise. BMVC, 2021.\n\n[3] Scan: Learning to classify images without labels, ECCV, 2020."}, {"review_id": "NojrqYObs9", "reviewer": "Reviewer_S4wz", "summary": "This paper presents DCBoost, a parameter-free plug-in module designed to enhance the global structure of deep clustering models by leveraging local neighborhood consistency. I believe using trustworthy local cues to guide global optimization is intuitive, novel, and practically relevant. The authors demonstrate improvements across several popular baselines and datasets. However, the work still exhibits several limitations in terms of robustness under real-world conditions, such as noise and imbalance, limited generalization beyond flat clustering, and incremental performance gains despite extensive evaluation.", "questions": "- Your method assumes local k-NN consistency is a reliable cue for selecting high-confidence samples. However, under class imbalance or label noise, the local neighborhood may become less trustworthy.\n- Is DCBoost applicable to hierarchical or density-based clustering models? If not, what are the obstacles?\n- Could you provide insights into real-world scenarios (e.g., medical, industrial) where such gains are critical?", "limitations": "- The assumption that the number of clusters is known in advance.\n- Potential performance degradation under imbalanced or non-uniform data distributions.\n- The lack of mechanisms to dynamically estimate or adapt to unknown class counts.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n- The work uses adaptive k-NN consistency to guide representation learning, which is a fresh and valuable perspective.\n- The proposed module does not modify network structures, making it highly practical for real-world and legacy systems.\n- The paper includes ablation studies on loss terms, adaptive k selection, and class weighting strategies, showing clear empirical support for each design choice.\n\n**Weaknesses:**\n- The method assumes that local structures are reliable, yet this assumption may break under class imbalance or noisy samples.\n- The framework seems incompatible or untested on models based on hierarchical clustering or density-based clustering, limiting generality.", "was_revised": true, "final_justification": "The author's answer resolved some of my concerns. I think it is good work. However, clustering should not only focus on accuracy, but also explore the discoveries or generalizations brought about by applications in real scenarios. This is also why deep clustering methods have not been popular. In practice, we prefer traditional clustering methods such as k-means.", "rebuttal": "Thank you for the reviewer’s constructive and thoughtful feedback. Below, we address each of your concerns one by one:\n\n**About incremental performance gains in deep clustering.**\n\nWe would like to emphasize that **DCBoost achieves consistent and significant performance improvements across a wide range of strong baselines**, despite already high starting points. This indicates that our method is capable of **further enhancing state-of-the-art results**, which is non-trivial in the unsupervised clustering domain.Importantly, under **strictly unsupervised conditions**, our method establishes **new state-of-the-art performance** across multiple standard benchmarks without using any true labels. In this context, **the observed gains are both meaningful and substantial.**\n\n\n**W1&Q1. The local neighborhood may become less trustworthy under class imbalance or label noise.**\n\nThank you for the insightful comment regarding the potential unreliability of local neighborhood consistency under class imbalance. But we would like to clarify that in our **unsupervised clustering** setting, the model does **not rely on ground-truth labels**, and thus is **not subject to conventional label noise** originating from human annotation errors. Instead, the labels are generated via self-supervised learning or clustering, which may contain **inherent uncertainty**, but this differs fundamentally from traditional noisy label scenarios. Therefore, while the neighborhood consistency may be affected by imperfect pseudo-labels, the concern of **external label noise** corrupting local structure is **not applicable** here. \n\n**Regarding class imbalance**, we have added a qualitative analysis of the local $k$-NN neighborhood structure on long-tailed dataset CIFAR10-LT and CIFAR20-LT, where the number of training samples per class follows an **exponential decay** controlled by a ratio between the number of samples in the most frequent class and the least frequent class. For instance, with ratio=10, the head class contains 5000 samples while the tail class has only 500.  As expected, local consistency does degrade slightly compared to the balanced case due to skewed class distributions. However, it still remains **significantly more reliable than global structural signals**, making it still a valuable cue for identifying high-confidence samples. Specifically, our results show that $k$-NN accuracy remain much higher compared to the current clustering ACC and better than global features, even in long-tailed scenarios: \n\n| CIFAR10-LT    | Intra-class ↑ | Inter-class ↓ | KNN Accuracy(%) ↑ | SC↑ | ACC (%) ↑ |\n| ------------- | ------------- | ------------- | -------------- | ------ | --------- |\n| ProPos | 0.188        | -0.017       | 87.5| 0.081  | 46.1      |\n| ProPos + Ours | **0.924**        | **-0.101**       | **95.4** | **0.682** | **47.4**|\n\n| CIFAR20-LT    | Intra-class ↑ | Inter-class ↓ | KNN Accuracy(%) ↑ | SC ↑ | ACC (%) ↑ |\n| ------------- | ------------- | ------------- | -------------- | ----- | --------- |\n| ProPos        | 0.156        | -0.009  | 70.3 | 0.088 | 42.1|\n| ProPos + Ours | **0.775**        | **-0.038**       | **70.8**  | **0.487** | **43.4** |\n\nTo further validate the robustness of our method, we conducted additional experiments on **CIFAR10-LT and CIFAR20-LT**. The following results demonstrate that our method consistently improves clustering performance over strong baselines:\n\n| Dataset (Ratio=10) |          | CIFAR10-LT |          |          | CIFAR20-LT |          |\n| ------------------ | -------- | ---------- | -------- | -------- | ---------- | -------- |\n| Method             | NMI      | ACC        | ARI      | NMI      | ACC        | ARI      |\n| ProPos        | 57.1     | 48.3       | 40.7     | 47.6     | 42.1       | 29.9     |\n| ProPos+Ours    | **61.3** | **49.9**   | **43.3** | **49.1** | **43.4**   | **31.5** |\n| CoNR           | 67.2     | 64.6       | 56.7     | 51.2     | 43.9       | 30.3     |\n| CoNR+Ours      | **68.4** | **65.3**   | **59.1** | **51.4** | **44.1**   | **33.3** |\n| LFSS [1]           | 57.9     | 56.2       | 43.0     | 46.7     | 41.4       | 28.3     |\n| LFSS+Ours      | **61.3** | **60.1**   | **46.9** | **47.5** | **42.4**   | **29.4** |\n\nThese results demonstrate that our $k$-NN-based selection strategy is **effective and robust, even when local consistency may be affected by class imbalance.**  Although we do not incorporate any specific mechanisms to address non-uniform data distributions scenarios, our method **still yields consistent improvements** across different datasets and baselines.\n\n\n**W2&Q2. Generality on the hierarchical clustering or density-based clustering model.**\n\nWhile our method was not originally tailored for hierarchical or density-based clustering—since flat clustering remains the mainstream in deep clustering—we recognize the importance of generalizability across paradigms. To this end, we have conducted additional experiments on CIFAR10 using a hierarchical clustering model. Although DCBoost does not leverage the hierarchical structure explicitly, we only retained the **pre-hierarchical representation part** of the model and integrated it into our own training framework. Based on these learned representations, we applied our training process. **Even without utilizing the full hierarchical pipeline**, DCBoost consistently improved the clustering performance, **outperforming both the original representation and the full hierarchical clustering baseline**:\n| Method                             |NMI      | ACC      | ARI      |\n| ---------------------------------- | -------- | -------- | -------- |\n| CoHiClust [2] (representation only)     | 75.5     | 82.5     | 70.5     |\n| CoHiClust(hierarchical clustering) | 77.8     | 83.7     | 72.9     |\n| CoHiClust(representation)+Ours | **77.9** | **84.4** | **73.6** |\n\nExtending **DCBoost** to **density-based clustering methods** (such as DBSCAN) presents additional challenges. These methods are **non-parametric** and do not assume a fixed number of clusters, making it difficult to align with DCBoost’s current reliance on pseudo-labels. However, adapting DCBoost to such methods remains a promising direction for future work.\n\n\n**Q3. Insights into real-world scenarios.**  \n\nWe thank the reviewer for highlighting the importance of real-world relevance. To this end, we evaluated our method on **PathMNIST**, a medical imaging dataset derived from colon pathology slides, involving the classification of different tissue types. Our method shows significant improvements over the baseline ProPos:\n| Method                             |NMI      | ACC      | ARI      |\n| ---------------------------------- | -------- | -------- | -------- |\n| ProPos|58.4| 67.6| 48.6|\n| ProPos+Ours | **63.7**|**72.9**| **55.3**|\nThe consistent improvements across all metrics suggest our method achieves more accurate and robust clustering. Such gains can help provide more stable and consistent pseudo-labels in weakly supervised or semi-supervised clinical workflows, and enhance the reliability of AI-assisted diagnosis or pre-screening.\nBeyond this,  in **industrial anomaly detection**, where labeled anomalies are rare, selecting trustworthy normal patterns through local consistency improves unsupervised model robustness. These scenarios rely on strong pseudo-supervision, making our framework’s improvements especially valuable in practice.\n\n\n**Limitation in estimating class counts.**\n\n**We would like to point out that the vast majority of existing deep clustering methods assume the number of clusters K as a predefined input.** Similarly, our method also operates under this common assumption. However, we included over-clustering and under-clustering experiments in Appendix B, Table 8 (e.g., CIFAR-20 with cluster numbers set to 10, 20, 30, 40, 50) that our method remains robust even when the specified cluster number deviates from the true number of classes. In particular, DCBoost consistently outperforms the baseline ProPos across all settings. These results suggest that, although our method does not explicitly estimate the number of clusters, it remains more robust to such inaccuracies compared to baseline methods.\nWe agree that incorporating mechanisms to dynamically estimate or adapt to unknown class counts is a valuable direction and consider this an interesting avenue for future work.\n\n[1] Learning from Sample Stability for Deep Clustering, ICML, 2025.\n\n[2] Contrastive Hierarchical Clustering, ECML-PKDD, 2023."}, {"review_id": "piaCJLQ7iI", "reviewer": "Reviewer_ZuXL", "summary": "The paper proposes a plug-and-play design for improving a pre-trained clustering method iteratively by its confident predictions. This is achieved by an adaptive k-nn selection mechanism for identifying confident samples with consistent prediction as their neighbours. By explicitly pulling those confident and potentially positive sample pairs together in the latent space and pushing the centres of different clusters further away, the proposed method is shown effective on boosting the clustering performance of a wide range of pretrained models.", "questions": "Since all the additional constraints introduced to the pre-trained model are applied to the features instead of their clustering predictions, it is interesting to see how the improvements made in this paper help with other representation learning benchmarks, e.g., are the improved features more generalisable to new data domains or new tasks?\n\nPlease also refer to the strengths and weaknesses section, where my main concerns are listed.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n+ The paper is generally well-written with clear motivation for improving the global cluster structure by the potentially reliable pairwise relationships drawn from the local structure.\n+ The proposed method is effective and has been shown generalisable to benefit extensive pre-trained models on various benchmarks.\n\nWeaknesses:\n+ Important related works & baselines missing. The proposed method follows the self-training/self-labelling paradigm to gradually improve itself with its own predictions. Such an idea is commonly seen in unsupervised training and deep clustering [1][2]. Those types of self-training/self-labelling methods can be applied to pre-trained models to obtain further improvements. There is only CoNR being discussed from this perspective, but a more comprehensive study and comparisons is essential.\n+ The “adaptive” k-NN filtering sweeps over all candidate values of “k” can be inefficient, especially when dealing with large-scale training data. The performance improvements it brings shall be further studied together with the increase in training cost.\n+ How does the positive loss help with mitigating imbalanced clustering is unclear to me. How does it help with assigning each class an equal contribution as claimed at L248?\n+ It is also unclear how the cluster prototypes are computed. What will happen to the clusters that have no sample in a mini-batch? This can happen when the cluster distribution is imbalanced or the number of clusters is greater than the batch size.\n\n\n[1] Caron, Mathilde, et al. \"Deep clustering for unsupervised learning of visual features.\" Proceedings of the European conference on computer vision (ECCV). 2018.  \n[2] Gansbeke, Van, et al. “Scan: Learning to classify images without labels.” Proceedings of the European Conference on Computer Vision (ECCV). 2020", "was_revised": true, "final_justification": "The discussion about other self-labelling methods and comparison to them is convincing, which addresses my primary concerns about the effectiveness of the proposed model. The additional runtime information provided by the authors answers my questions about efficiency. The evaluation of the feature's generalisation ability to other tasks demonstrates the potential impact of this work beyond clustering. Given all these, I am happy to improve my rating of this work.\n\nBesides, I would still like to encourage the authors to dig deeper into the performance degradation issue when applying other self-labelling methods to pre-learned features, e.g., whether this can be avoided by freezing the network partially or at the early training stage.", "rebuttal": "We sincerely thank the reviewer for the careful evaluation and constructive feedback on our work. Below, we provide detailed responses to each of the key concerns raised.\n\n**W1. More self-labelling paradigm.**\n\nThanks for your insightful comment. Our paper has already included comparisons with CoNR, a more recent and relevant representative of this paradigm. Follow the reviewer's suggestion, we additionally implemented **self-labeling pipelines based on DeepCluster[1] and SCAN[2]** on the ProPos baseline. Specifically, for SCAN, we used pretrained ProPos representations and performed the second and third training phases of SCAN; for DeepCluster, we attached a randomly initialized clustering head and supervised the model using self-generated pseudo-labels. \n\nThe results on **CIFAR10** (shown below) indicate that **these self-labeling methods do not improve performance** over the ProPos baseline. In contrast, our method consistently yields significant gains. This suggests that existing self-labeling paradigms may not always transfer effectively to pretrained models.\n\n|Method|NMI|ACC|ARI|\n|-|-|-|-|\n|ProPos|88.1|94.4|88.3|\n|ProPos+SCAN-2[2]| 87.3|93.0|85.9|\n|ProPos+SCAN-2+SCAN-3[2]|87.1|93.1|86.0|\n|ProPos+DeepCluster[1]|86.5|93.6|86.6|\n|ProPos+Ours|**91.1**|**96.0**|**91.6**|\n\nOne potential reason why DeepCluster and SCAN underperform is **their reliance on joint training with a randomly initialized clustering head**. Prior work CDC[3] has shown that **attaching randomly initialized heads can destabilize pre-trained representations**. Therefore, while these methods work well in end-to-end pipelines with consistent optimization objectives, **they may degrade performance when grafted onto pretrained models without adaptation**. By contrast, our method preserves the pretrained model and only introduces a lightweight asymmetric predictor, which minimizes disruption and maintains training stability. This design enables effective improvement on strong pretrained features without architectural modification.\n\nWe hope this clarifies that off-the-shelf application of clustering-head based self-labeling methods is not universally effective, and underscores the novelty and necessity of our method. Full results and analysis will be included in the discussion part of main paper in the final version. \n\n**W2. About training cost of adaptive k-NN filtering.**\n\nOur adaptive $k$-NN filtering introduces **negligible computational overhead**. Specifically, the top‑$m$ neighbors are retrieved **only once per batch**, and scores for all candidate $k$ (from 1 to $m$) are computed **based on this single result**. The optimal-$k$ selection relies on **simple, vectorized label consistency checks over the retrieved indices** (see function $find optimal-k-balanced$  in code). This process has a time complexity of **$O(B·m)$**, where $B$ is the batch size and $m$ is the maximum number of neighbors considered. Notably, **this step is independent of the total dataset size $N$**. Therefore, our method avoids the inefficiencies typically associated with exhaustive sweeping over multiple k values and remains scalable to large datasets.\n\nTo validate this, we have conducted experiments on CIFAR-10 with settings: using no filtering ($k$ = 0), fixed $k=25$, and adaptive selection with $m=25,50,75$. As shown below, **training time remains nearly identical across all variants**, but get great performance improvements:\n\n|Variant |NMI|ACC|ARI|Time(h:min:s)|\n|-|-|-|-|-|\n|ProPos|88.1|94.4|88.3|-|\n|$k=0$|89.1|94.9|89.3|4:22:32|\n|$k=25$|91.1|96.0|91.6|4:25:58|\n|$m=25$|91.2|96.1|91.7|4:25:51|\n|$m=50$|91.1|96.0|91.6|4:29:00|\n|$m=75$|91.1|96.0|91.6|4:25:58|\n\nThe results demonstrate that our adaptive filtering strategy is both **efficient and effective**, providing performance benefits without introducing additional training cost.\n\n**W3. How does the positive loss help with mitigating imbalanced clustering?**\n\nThe **positive loss** is designed to promote intra-class compactness among **high-confidence samples**. However, some classes may dominate this loss simply because they have more confident samples. To address this, we introduce a **per-class normalization weight** $w_c$, which ensures that **each class contributes equally** to the overall loss, regardless of its size. Specifically, $w_c$ can be seen as the **inverse** of the square of the L2 norms of the aggregated features from class c. **When a class has more high-confidence samples (thus a larger summed feature norm), its influence on the loss is automatically reduced.** In this way, the loss naturally compensates for the **imbalance in the number of high-confidence samples** across classes.\n\n**W4-1. How to compute cluster prototypes?**\n\nAs described in the paper, cluster prototypes are computed by **averaging the features of high-confidence samples within each pseudo-label class in current training batch**, followed by L2 normalization. The number of prototypes $c_B$ in a batch corresponds to the number of classes with confident samples present in that batch.\n\n**W4-2. What will happen to the clusters that have no sample in a mini-batch?**\n\nIf a cluster has no sample in a mini-batch **when the class distribution is imbalanced,** we simply exclude that class from the current loss computation. This design does not affect the stability of training, since the model learns from the cumulative gradients over many batches rather than requiring every batch to contain all classes.\n\nImportantly, we additionally validated the method's robustness on long-tailed benchmarks CIFAR10-LT and CIFAR20-LT, where class sizes follow an **exponential decay** (e.g., with ratio=10, head has 5000 samples, tail only 500).Our method still consistently outperforms the baselines. This demonstrates that the occasional absence of some classes in individual batches does not lead to significant degradation in overall clustering performance.\n\n|Ratio=10||CIFAR10-LT|||CIFAR20-LT||\n|-|-|-|-|-|-|-|\n|Method| NMI| ACC|ARI|NMI|ACC|ARI|\n|ProPos|57.1|48.3|40.7|47.6|42.1|29.9|\n|ProPos+Ours|**61.3**|**49.9**|**43.3**|**49.1**|**43.4**|**31.5**|\n|CoNR|67.2|64.6|56.7|51.2|43.9|30.3|\n|CoNR+Ours|**68.4**|**65.3**|**59.1**|**51.4**|**44.1**|**33.3**|\n|LFSS [4]|57.9|56.2|43.0|46.7|41.4|28.3|\n|LFSS+Ours|**61.3**|**60.1**|**46.9**|**47.5**|**42.4**|**29.4**|\n\nWe added per-class accuracy analysis on **CIFAR20-LT**,  where C0 is the most frequent (head) class and C19 is the rarest (tail) class. We compared the class-wise accuracy before and after integrating our method:\n|Method\\Class|C0|C1|C2|C3|C4|C5|C6|C7|C8|C9|C10|C11|C12|C13|C14|C15|C16|C17|C18|C19|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|ProPos|33.1|24.5|58.6|30.9|66.9|25.1|43.3|69.0|37.3|62.2|73.9|53.1|33.6|6.8|81.2|12.4|1.4|90.9|0.0|0.8|\n|ProPos+Ours|**34.9**|**26.3**|**59.8**|**31.5**|**69.5**|23.0|43.1|**70.4**|36.5| **66.4**|**74.2**| 52.5| **36.2**|**7.5**| **84.7**|**14.6**|1.4|**91.6**|**12.4**|0.0|\n\n**Our method brings improvements in the majority of classes; importantly, it is not restricted to head classes only**. Several tail classes, which are more likely to be absent from some mini-batches, still get noticeable performance gains.\nBeyond class imbalance, we acknowledge the challenges of scaling to many clusters. Our primary focus is on moderate-scale settings where batch sizes can cover most classes. In response to the reviewer’s concern, we have newly added experiments in a setting where **the number of clusters exceeds the batch size**. Although our base selection method may face limitations due to batch size constraints, it can still be extended to these challenging settings by incorporating a memory bank to store and reuse features across batches, and adopting a fixed $k$-NN retrieval method to stabilize neighbor selection.\nWe add experiments on **CIFAR100**, where we intentionally reduce the batch size to 64 (i.e., fewer than the number of classes) to simulate this, and size 256 on **ImageNet-1K**, a large-scale dataset with 1,000 categories. Because full training of ImageNet-1K is resource-intensive, we used ProPos to train for 30 epochs and added our method for another 10 epochs.\n|Dataset||CIFAR100|||ImageNet-1K||\n|-|-|-|-|-|-|-|\n|Method|NMI|ACC|ARI|NMI|ACC|ARI|\n|Propos|65.7|55.2|40.1|51.3|22.0|13.4|\n|Propos+Ours|**67.1**|**57.4**|**43.8**|**54.5**|**23.2**|**15.1**|\n\nOur method still improves clustering performance across all metrics on both datasets. It is also indicated that **using high-quality local information to guide the optimization of global structure** remains effective, even when not all classes are present in each mini-batch. We will include the results and analysis in the final version of our paper.\n\n**Q1. Are the improved features more generalizable to new data domains or new tasks?**\n\nTo evaluate whether our improved representations are more generalizable, we follow standard linear evaluation protocols. We freeze the learned features and train a linear classifier on top of them for downstream classification. Specifically, we conduct two experiments:\n\n(1) **In-domain generalization:** Linear classification on CIFAR10 using features learned on training set of CIFAR10. \n(2) **Cross-domain generalization:** Linear classification on STL10 using features learned on CIFAR10.\n\nWe report Top-1 accuracy and Top-5 accuracy as performance metrics.\n\nAs shown below, our method consistently improves performance over the baseline in both settings, suggesting that the enhanced features are not only more discriminative within the same domain, but also more transferable to related domains.\n\n|CIFAR10|Top1|Top5|\n|-|-|-|\n|ProPos|94.7|99.8|\n|ProPos+Ours|**95.0**|99.8|\n\n|STL10|Top1|Top5|\n|-|-|-|\n|ProPos|76.2|98.8|\n|ProPos+Ours|**77.6**|98.6|\n|CoNR|67.0|96.2|\n|CoNR+Ours|**73.6**|**98.1**|\n\n[3]Towards Calibrated Deep Clustering Network, ICLR, 2025.\n\n[4]Learning from Sample Stability for Deep Clustering, ICML, 2025."}, {"review_id": "gudPTxDb9D", "reviewer": "Reviewer_Tv3M", "summary": "The paper proposes refining pre-trained Deep Clustering networks with plug-in losses, namely an instance consistency loss (inspired from self-supervisedlearning) and a positive (negative) loss that bring closer (futher) the representations of samples (or the class protypes of samples) based on their pseudo-labels. A central point in the paper is that the two latter losses are applied only on \"reliable\" samples -- those are identified as such based on consistency in the pseudolabels) of their k-NN neighboors. The method shows improvements in relation to the SOTA.", "questions": "I would appreciate to bring this work in the context of other works that perform sample selection/filtering in the domain of classification and in particular with the ones that utilize k-NN for doing so, and address the comment on scaling with the number of clusters.\n\n\"By incorporating an adaptive k-NN sample selection, our method strikes an optimal trade-off between the quality and quantity of self-supervision signals.\" It is not supported that the tradeoff is optimal in some sense. We experimentally see that it leads to improvemnts in some metrics, but that is not the same as claiming optimality.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Positive:\n-- The method is simple and the paper well written.\n-- There is some novelty in the application of reliable sample sample selection and its use in the selective application of the losses in the domain of clustering.\n-- The losses can be applied so as to refine existing models. Experiments show that when the losses are applied in several models, the improvements are consistent, albeit not always large.\n\nNegative:\n\n-- The paper essentially brings sample selection methods from the domain of classification to the domain of clustering. This has some novelty as I methioned in my view. However,  sample selection based on consistency of the labels of the samples in the local neighborhood is not new (e.g, [a1][a2]) -- I believe that the paper should at least refer to such works. Also, it is not clear whether other methods for sample selection, including the ones mentioned in the paper itself would give competitive performance. As it stands, I feel that the works in the domain learning with noisy labels (for classification) is not well covered. \n\n-- The results presented are on datasets with a small number of clusters (10-20). It is unclear how the method scales with the number of clusters.\n\n-- The method seems to need to perform k-means clustering. This introduces additional computational complexity.\n\n[a1] Diego Ortego, Eric Arazo, Paul Albert, Noel E O’Connor, and Kevin McGuinness. Multi-objective interpolation training for robustness to label noise. In Proceedings of\nthe IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021.\n[a2] Ssr: An efficient and robust framework for learning with unknown label noise, C Feng, G Tzimiropoulos, I Patras, BMVC, 2021", "was_revised": true, "final_justification": "The paper has novelty and good experimental results. The response addressed my concerns wrt to missing sota and experiments and I am happy to recommend acceptance.", "rebuttal": "We sincerely thank the reviewer for their constructive comments and thoughtful suggestions. We appreciate the recognition of the clarity and simplicity of our method. Regarding the main concerns, we provide detailed responses as follows:\n\n**N1&Q1-1. Relation to existing works in noisy label learning with local consistency-based selection.**\n\nThanks for the reviewer's comments. **Sample selection is a fundamental and important problem**, especially in unsupervised learning and deep clustering. Related work can not diminish the novelty of our method, which **introduces clear innovations and provides advantages** beyond existing methods designed for the domain of noisy label learning.\n\nMOIT[a1] detects noisy labels by measuring the disagreement between a sample’s original label and the class distribution of its k-nearest neighbors in the global feature space. And SSR[a2] selects clean samples based on label–neighborhood consistency and uses a separate classifier to relabel samples with high confidence. **They all need do $k$-NN search in the whole dataset with a selected fixed k, and may have some hyperparameters like consistency and confidence thresholds.** However, our method is a parameter-free sample selection method and applies local adaptive k-nn within the mini-batch instead of the whole dataset.\nTo provide a fair comparison, we **re-implemented MOIT and SSR** sample selection strategies and applied them on top of ProPos, replacing our selection module while keeping the rest of the training unchanged. Notably, SSR combines both $k$-NN filtering and confidence thresholding, which may not generalize to all baseline methods (e.g., those without a classifier head). Therefore, we only adopted its **$k$-NN-based filtering component** for compatibility.\n\n\n| Dataset         |          | CIFAR10  |          |          | CIFAR20  |          |          | ImageNet-Dogs |          |\n| --------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | ------------- | -------- |\n| Method  | NMI | ACC  | ARI  | NMI  | ACC  | ARI  | NMI | ACC    | ARI |\n| ProPos          | 81.1     | 94.4     | 88.3     | 60.7     | 61.6     | 44.4     | 72.7     | 76.9          | 66.4     |\n| ProPos + Ours(MOIT selection) | 90.7     | 95.8     | 91.1     | 64.1     | 63.2     | 47.8     | 73.5     | 77.1          | 66.6     |\n| ProPos + Ours(SSR selection)    | 90.9     | 95.9     | 91.3     | 63.9     | 63.6     | 48.3     | 75.3     | 79.0          | 70.1     |\n| ProPos + Ours   | **91.1** | **96.0** | **91.6** | **64.5** | **63.9** | **49.2** | **76.3** | **79.7**     | **70.7** |\n\nTo further ensure fairness with SSR, we conducted **additional CIFAR10 and ImageNet-Dogs experiments on the CDC baseline** by replacing our sample selection with full SSR filtering with confidence thresholding:\n\n| Dataset    |          | CIFAR10  |          |          | ImageNet-Dogs |          |\n| ---------- | -------- | -------- | -------- | -------- | ------------- | -------- |\n| Method |NMI |ACC|ARI|NMI |ACC|ARI|\n| CDC        | 89.3     | 94.9     | 89.5     | 76.8     | 79.2          | 70.2     |\n| CDC + Ours(SSR selection)  | 89.8     | 95.0     | 89.9     | 77.3     | 79.6          | 70.9     |\n| CDC + Ours | **89.9** | **95.1** | **90.0** | **77.5** | **79.7**   | **71.4** |\n\nMoreover, we compared their full pipelines with ours (including both filtering and loss function) on **CIFAR10** and observed that **the overall performance gain from MOIT and SSR remains consistently lower than ours,  even not better than ProPos**. Additionally, **our method achieves better results with shorter training time**, as shown below:\n\n\n| Method      | NMI      | ACC      | ARI      | Time(hour) |\n| ----------- | -------- | -------- | -------- | ---------- |\n| ProPos      | 88.1     | 94.4     | 88.3     | -          |\n| ProPos+MOIT[a1]       | 88.0     | 94.4     | 88.3     | -          |\n| ProPos+SSR[a2]         | 86.8     | 92.5     | 85.2     | -          |\n| ProPos+Ours(MOIT selection) | 90.7     | 95.8     | 91.1     | 5.0          |\n| ProPos+Ours(SSR selection)  | 90.9     | 95.9     | 91.3     | 4.8        |\n| ProPos+Ours | **91.1** | **96.0** | **91.6** | **4.5**    |\n\nWhile both MOIT and SSR improve over the baseline, their performance is **consistently inferior to our method**. We believe this is because **our framework is jointly optimized with the selection strategy and a discriminative loss**, which is not the case for methods like MOIT or SSR. When their selection is applied using their own loss to train the model, performance does not improve. In addition, **global $k$-NN filtering**, as used in MOIT and SSR, introduces extra computational cost, while our method remains **efficient and scalable** without additional complexity or hyperparameters.  \nFollowing your suggestion, we will refer to the related works and add the results and analysis in the discussion part of our final paper.\n\n\n\n**N2&Q1-2. How the method scales with the number of clusters?**  \n\nThank you for the valuable comment on scalability. As shown in the main paper (Table 2), our method consistently outperforms baselines on datasets with 10–20 categories, which already demonstrates its strong performance in small- to medium-scale clustering scenarios.\n\nTo further demonstrate the scalability of our method to datasets with a large number of categories, we additionally report results on **CIFAR100**, **Tiny-ImageNet (200 classes, more results are in Appendix B)**, and **ImageNet-1K (1000 classes)**. As shown below, our method consistently improves over the ProPos baseline across all datasets:\n| Dataset     |         | CIFAR100 |          |          | Tiny-ImageNet |          |          | ImageNet-1K |          |\n| ----------- | ------- | -------- | -------- | -------- | ------------- | -------- | -------- | ----------- | -------- |\n| Method | NMI | ACC  | ARI  | NMI  | ACC       | ARI  | NMI  | ACC     | ARI  |\n| ProPos      | 65.7    | 55.2     | 40.1     | 46.5     | 29.8          | 18.2     | 51.3     | 22.0        | 13.4     |\n| ProPos+Ours | **67.4** | **57.1** | **43.4** | **48.9** | **31.9**      | **19.8** | **54.5** | **23.2**    | **15.1** |\n\nThese results confirm that our method is scalable and remains effective even when the number of clusters increases from tens to hundreds or thousands.\n**Importantly, our method is theoretically agnostic to the number of classes**. What fundamentally limits performance is not the number of categories itself, but the memory constraints that restrict batch size. When batch size is small relative to the number of classes, the chance of finding same-class neighbors per sample decreases. By introducing memory bank, our method compensates for this limitation without relying on strong alteration or architectural changes.\nAnd we will include these experiments in our final version.\n\n\n**N3. Additional computational complexity for k-means.**\n\nWe appreciate the reviewer’s concern regarding the computational overhead introduced by $k$-means clustering. It is important to clarify that **$k$-means is not a contribution of our method but rather a commonly used approach for obtaining pseudo-labels** in representation-based deep clustering models. Moreover, **our method is flexible and does not rely strictly on $k$-means:** for clustering-head based models, we can replace $k$-means with a direct SoftMax classification layer to generate pseudo-labels.\n\nAs demonstrated in Appendix C, Table 9, for the clustering-head baseline CDC, incorporating our method yields consistent improvements regardless of whether $k$-means or SoftMax is used for label assignment.  \n\n\n**Q2. The tradeoff is not always optimal in some sense.**  \n\nThank you for pointing this out. We agree that the term \"optimal\" may overstate the result without theoretical or exhaustive empirical justification. We will revise the sentence in the paper to avoid this claim and now state that our adaptive $k$-NN sample selection achieves a _favorable_ trade-off between the quality and quantity of self-supervision signals, as demonstrated by consistent performance improvements in our experiments."}]

Author rebuttal:

Meta-review: This paper presents DCBoost, a novel deep clustering enhancement method for improving clustering performance by refining the global structure of existing deep clustering models. One of the clear (practical) advantages of this approach is that it can be plugged into existing clustering models and doesn’t require hyper-param tuning. The authors also demonstrate the gains from this technique when combined with existing clustering models evaluated across multiple datasets. 

Overall, the paper is interesting and the authors provide good clarity & additional details in the discussion that address many of the reviewer concerns. There are several strengths already highlighted by the reviewers. A few of the weaknesses (also ack’d by authors) is the performance degradation under different settings (eg high imbalance, non-uniform data dist.) The authors provide some (limited) experiment results in the discussion to address this. If anything, one of the areas that could benefit from additional work is extending the empirical evaluations to large(r) benchmark datasets and going beyond simple, image datasets (e.g, language tasks with global structure) — there are several publicly available at this point. It would help further strengthen the empirical claims as well as practical applicability of the proposed approach. The authors are also encouraged to incorporate all feedback from the reviews+discussions in the final version of the paper.
Decision: Accept (poster)