|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM389469106 |
| 003 |
DE-627 |
| 005 |
20251007231847.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
250716s2025 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2025.3588239
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1591.xml
|
| 035 |
|
|
|a (DE-627)NLM389469106
|
| 035 |
|
|
|a (NLM)40663672
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Wu, Jianlong
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a ClusMatch
|b Improving Deep Clustering by Unified Positive and Negative Pseudo-Label Learning
|
| 264 |
|
1 |
|c 2025
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 06.10.2025
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Recently, deep clustering methods have achieved remarkable results compared to traditional clustering approaches. However, its performance remains constrained by the absence of annotations. A thought-provoking observation is that there is still a significant gap between deep clustering and semi-supervised classification methods. Even with only a few labeled samples, the accuracy of semi-supervised learning is much higher than that of clustering. Given that we can annotate a small number of samples in a certain unsupervised way, the clustering task can be naturally transformed into a semi-supervised setting, thereby achieving comparable performance. Based on this intuition, we propose ClusMatch, a unified positive and negative pseudo-label learning based semi-supervised learning framework, which is pluggable and can be applied to existing deep clustering methods. Specifically, we first leverage the pre-trained deep clustering network to compute predictions for all samples, and then design specialized selection strategies to pick out a few high-quality samples as labeled samples for supervised learning. For the unselected samples, the novel unified positive and negative pseudo-label learning is introduced to provide additional supervised signals for semi-supervised fine-tuning. We also propose an adaptive positive-negative threshold learning strategy to further enhance the confidence of generated pseudo-labels. Extensive experiments on six widely-used datasets and one large-scale dataset demonstrate the superiority of our proposed ClusMatch. For example, ClusMatch achieves a significant accuracy improvement of 5.4% over the state-of-the-art method ProPos on an average of these six datasets
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Li, Zihan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Sun, Wei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yin, Jianhua
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Nie, Liqiang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lin, Zhouchen
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 47(2025), 11 vom: 06. Okt., Seite 9688-9701
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
|
| 773 |
1 |
8 |
|g volume:47
|g year:2025
|g number:11
|g day:06
|g month:10
|g pages:9688-9701
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2025.3588239
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 47
|j 2025
|e 11
|b 06
|c 10
|h 9688-9701
|