{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T05:36:07Z","timestamp":1764308167694,"version":"3.46.0"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12473105, 12473106"],"award-info":[{"award-number":["12473105, 12473106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Projects of Science and Technology Cooperation and Exchange of Shanxi Province","award":["202204041101037, 202204041101033"],"award-info":[{"award-number":["202204041101037, 202204041101033"]}]},{"name":"The central government guides local funds for science and technology development","award":["YDZJSX2024D049"],"award-info":[{"award-number":["YDZJSX2024D049"]}]},{"name":"the science research grant from the China Manned Space Project","award":["CMS-CSST-2021-B03"],"award-info":[{"award-number":["CMS-CSST-2021-B03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Deep clustering aims to discover meaningful data groups by jointly learning representations and cluster probability distributions. Yet existing methods rarely consider the underlying information characteristics of these distributions, causing ambiguity and redundancy in cluster assignments, particularly when different augmented views are used. To address this issue, this paper proposes a novel information-principled deep clustering framework for learning invariant, redundancy-reduced, and discriminative cluster probability distributions, termed ICIRD. Specifically, ICIRD is built upon three complementary modules for cluster probability distributions: (i) conditional entropy minimization, which increases assignment certainty and discriminability; (ii) inter-cluster mutual information minimization, which reduces redundancy among cluster distributions and sharpens separability; and (iii) cross-view mutual information maximization, which enforces semantic consistency across augmented views. Additionally, a contrastive representation mechanism is incorporated to provide stable and reliable feature inputs for the cluster probability distributions. Together, these components enable ICIRD to jointly optimize both representations and cluster probability distributions in an information-regularized manner. Extensive experiments on five image benchmark datasets demonstrate that ICIRD outperforms most existing deep clustering methods, particularly on fine-grained datasets such as CIFAR-100 and ImageNet-Dogs.<\/jats:p>","DOI":"10.3390\/e27121200","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T15:56:41Z","timestamp":1764172601000},"page":"1200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ICIRD: Information-Principled Deep Clustering for Invariant, Redundancy-Reduced and Discriminative Cluster Distributions"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1228-2650","authenticated-orcid":false,"given":"Aiyu","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Electronic Information and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"},{"name":"School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1735-6797","authenticated-orcid":false,"given":"Robert M. X.","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1021-0744","authenticated-orcid":false,"given":"Yupeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8499-6017","authenticated-orcid":false,"given":"Yanting","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","unstructured":"Ren, Y., Pu, J., Yang, Z., Xu, J., Li, G., Pu, X., Yu, P.S., and He, L. (2022). Deep Clustering: A Comprehensive Survey. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39501","DOI":"10.1109\/ACCESS.2018.2855437","article-title":"A Survey of Clustering with Deep Learning: From the Perspective of Network Architecture","volume":"6","author":"Min","year":"2018","journal-title":"IEEE Access"},{"key":"ref_3","unstructured":"Aljalbout, E., Golkov, V., Siddiqui, Y., Strobel, M., and Cremers, D. (2018). Clustering with Deep Learning: Taxonomy and New Methods. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ohl, L., Mattei, P.A., and Precioso, F. (2025). A tutorial on discriminative clustering and mutual information. arXiv.","DOI":"10.1145\/3748255"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, S., Xu, H., Zheng, Z., Chen, J., Li, Z., Bu, J., Wu, J., Wang, X., Zhu, W., and Ester, M. (2024). A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions. arXiv.","DOI":"10.1145\/3689036"},{"key":"ref_6","unstructured":"Xie, J., Girshick, R., and Farhadi, A. (2016, January 20\u201322). Unsupervised Deep Embedding for Clustering Analysis. Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, NY, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, J., Parikh, D., and Batra, D. (2016, January 27\u201330). Joint unsupervised learning of deep representations and image clusters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.556"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., and Douze, M. (2018, January 8\u201314). Deep Clustering for Unsupervised Learning of Visual Features. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref_9","unstructured":"Ji, X., Henriques, J.F., and Vedaldi, A. (November, January 27). Invariant Information Clustering for Unsupervised Image Classification and Segmentation. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_10","unstructured":"Parulekar, A., Collins, L., Shanmugam, K., Mokhtari, A., and Shakkottai, S. (2023, January 12\u201315). InfoNCE Loss Provably Learns Cluster-Preserving Representations. Proceedings of the Thirty Sixth Conference on Learning Theory, Bangalore, India. Proceedings of Machine Learning Research."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_12","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), Virtual."},{"key":"ref_13","unstructured":"Li, Y., Wang, L., Wang, Y., Liu, T., and Zhang, L. (2021, January 2\u20139). Contrastive Clustering. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Virtually."},{"key":"ref_14","unstructured":"Liu, Y., Tu, W., Zhou, S., Liu, X., Song, L., Yang, X., and Zhu, E. (March, January 22). Deep Graph Clustering via Dual Correlation Reduction. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Online."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TETCI.2024.3353598","article-title":"Deepclue: Enhanced deep clustering via multi-layer ensembles in neural networks","volume":"8","author":"Huang","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9472","DOI":"10.1109\/TCSVT.2024.3399596","article-title":"Deep clustering with hybrid-grained contrastive and discriminative learning","volume":"34","author":"Huang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110032","DOI":"10.1016\/j.patcog.2023.110032","article-title":"Contrastive clustering with a graph consistency constraint","volume":"146","author":"Zhao","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chang, J., Wang, L., Meng, G., Xiang, S., and Pan, C. (2017, January 22\u201329). Deep adaptive image clustering. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.626"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nassar, I., Karlinsky, L., Feris, R., Tay, Y., Padhy, S., Noy, A., Zhang, L., Elhoseiny, M., Tsai, Y.H., and Nevatia, R. (2023, January 17\u201324). ProtoCon: Pseudo-Label Refinement via Online Clustering and Prototypical Consistency. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01120"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5847","DOI":"10.1109\/TIT.2010.2068870","article-title":"Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization","volume":"56","author":"Nguyen","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_21","unstructured":"Belghazi, M.I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D. (2018, January 10\u201315). Mutual Information Neural Estimation. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_22","unstructured":"Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y. (2019, January 6\u20139). Learning Deep Representations by Mutual Information Estimation and Maximization. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_23","unstructured":"Hu, W., Miyato, T., Tokui, S., Matsumoto, E., and Sugiyama, M. (2017, January 6\u201311). Learning Discrete Representations via Information Maximizing Self-Augmented Training. Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_24","unstructured":"Wu, J., Long, K., Wang, F., Qian, C., Li, C., Lin, Z., and Zha, H. (November, January 27). Deep comprehensive correlation mining for image clustering. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, S., Wang, C., and Yu, X. (2021, January 19\u201327). Deep Descriptive Clustering: Unifying Representation, Interpretability, and Discriminability. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/460"},{"key":"ref_26","unstructured":"Zhang, L., Li, H., Chen, Q., and Zhang, W. (November, January 29). Mutual Information-Driven Multi-View Clustering. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yan, X., Jin, Z., Han, F., and Ye, Y. (2024, January 16\u201322). Differentiable information bottleneck for deterministic multi-view clustering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02590"},{"key":"ref_28","unstructured":"Lou, Z., Zhang, K., Wu, Y., and Hu, S. (2025, January 13\u201319). Super Deep Contrastive Information Bottleneck for Multi-modal Clustering. Proceedings of the Forty-Second International Conference on Machine Learning, Vancouver, BC, Canada."},{"key":"ref_29","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A. (2020, January 6\u201312). Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS), Online."},{"key":"ref_30","unstructured":"Li, J., Zhou, P., Xiong, C., and Hoi, S.C.H. (2021, January 4\u20138). Prototypical Contrastive Learning of Unsupervised Representations. Proceedings of the Ninth International Conference on Learning Representations: ICLR 2021, Vienna, Austria."},{"key":"ref_31","unstructured":"Chuang, C., Robinson, J., Lin, Y., Torralba, A., and Jegelka, S. (2020, January 6\u201312). Debiased Contrastive Learning. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Online."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Van Gansbeke, W., Vandenhende, S., Georgoulis, S., Proesmans, M., and Van Gool, L. (2020, January 23\u201328). Learning to Classify Images without Labels. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58607-2_16"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7172","DOI":"10.1109\/TIP.2022.3221290","article-title":"SPICE: Semantic Pseudo-Labeling for Image Clustering","volume":"31","author":"Niu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhong, H., Wu, J., Chen, C., Huang, J., Deng, M., Nie, L., Lin, Z., and Hua, X.-S. (2021, January 11\u201317). Graph Contrastive Clustering. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00909"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109470","DOI":"10.1016\/j.patcog.2023.109470","article-title":"Strongly augmented contrastive clustering","volume":"139","author":"Deng","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110065","DOI":"10.1016\/j.patcog.2023.110065","article-title":"Deep image clustering with contrastive learning and multi-scale graph convolutional networks","volume":"146","author":"Xu","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Znalezniak, M., Rola, P., Kaszuba, P., Tabor, J., and \u015amieja, M. (2023, January 18\u201322). Contrastive hierarchical clustering. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Turin, Italy.","DOI":"10.1007\/978-3-031-43412-9_37"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gray, R.M. (2011). Entropy and Information Theory, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-7970-4"},{"key":"ref_39","unstructured":"Tishby, N., Pereira, F.C., and Bialek, W. (2000). The Information Bottleneck Method. arXiv."},{"key":"ref_40","unstructured":"Grandvalet, Y., and Bengio, Y. (2004, January 13\u201318). Semi-supervised Learning by Entropy Minimization. Proceedings of the 18th International Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_41","unstructured":"Krizhevsky, A., and Hinton, G. (2025, November 03). Learning Multiple Layers of Features from Tiny Images. Available online: https:\/\/bibbase.org\/network\/publication\/krizhevsky-hinton-learningmultiplelayersoffeaturesfromtinyimages-2009."},{"key":"ref_42","unstructured":"Coates, A., Ng, A., and Lee, H. (2011, January 11\u201313). An analysis of single-layer networks in unsupervised feature learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. JMLR Workshop and Conference Proceedings."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_44","first-page":"583","article-title":"Cluster Ensembles\u2014A Knowledge Reuse Framework for Combining Multiple Partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing Partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classif."},{"key":"ref_46","first-page":"5549","article-title":"Contrastive learning with stronger augmentations","volume":"45","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_48","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., and Lin, D. (2018, January 18\u201323). Unsupervised feature learning via non-parametric instance discrimination. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, J., Gong, S., and Zhu, X. (2020, January 14\u201319). Deep semantic clustering by partition confidence maximisation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00887"},{"key":"ref_51","unstructured":"Zhong, H., Chen, C., Jin, Z., and Hua, X.S. (2020). Deep robust clustering by contrastive learning. arXiv."},{"key":"ref_52","unstructured":"Tao, Y., Takagi, K., and Nakata, K. (2021). Clustering-friendly representation learning via instance discrimination and feature decorrelation. arXiv."},{"key":"ref_53","unstructured":"Dang, Z., Deng, C., Yang, X., and Huang, H. (2021). Doubly contrastive deep clustering. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"109507","DOI":"10.1016\/j.knosys.2022.109507","article-title":"Improved deep clustering model based on semantic consistency for image clustering","volume":"253","author":"Zhang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_55","unstructured":"Khosla, A., Jayadevaprakash, N., Yao, B., and Fei-Fei, L. (2011, January 20\u201325). Novel Dataset for Fine-Grained Image Categorization. Proceedings of the First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA."},{"key":"ref_56","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (June, January 2). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the NAACL, Minneapolis, MN, USA."},{"key":"ref_57","unstructured":"He, P., Liu, X., Gao, J., and Chen, W. (2021, January 3\u20137). DeBERTa: Decoding-enhanced BERT with Disentangled Attention. Proceedings of the ICLR, Virtual Event, Austria."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guedes, G.B., and da Silva, A.A.E. (2024). Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning. arXiv.","DOI":"10.5121\/csit.2023.131923"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., and Chen, D. (2021, January 7\u201311). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of the EMNLP, Virtual Event\/Punta Cana, Dominican Republic.","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"ref_60","unstructured":"Yan, Y., Zhang, Y., Lin, X., and Li, X. (2022, January 22\u201327). CoCLR-Text: Contrastive Cross-View Learning for Text Clustering. Proceedings of the Findings of ACL, Dublin, Ireland."},{"key":"ref_61","unstructured":"Huang, X., Khetan, A., Cvitkovic, M., and Bansal, V. (2020, January 6\u201312). TabTransformer: Tabular Data Modeling Using Contextual Embeddings. Proceedings of the NeurIPS, Virtual."},{"key":"ref_62","unstructured":"Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. (2021, January 6\u201314). Revisiting Deep Learning Models for Tabular Data. Proceedings of the NeurIPS, Virtual."},{"key":"ref_63","unstructured":"Ucar, T., Artelt, A., and Hammer, B. (2023, January 23\u201329). Self-Supervised Learning for Tabular Data via Masked Feature Reconstruction. Proceedings of the ICML, Honolulu, HI, USA."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Fini, E., Lathuili\u00e8re, S., Sangineto, E., Zhong, Z., Nabi, M., Sebe, N., and Ricci, E. (2021, January 20\u201325). A Unified Objective for Novel Class Discovery. Proceedings of the CVPR, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00915"},{"key":"ref_65","unstructured":"Vaze, S., De Melo, N.C., Bojanowski, P., Joulin, A., and Douze, M. (December, January 28). Generalized Category Discovery. Proceedings of the NeurIPS, New Orleans, LA, USA."},{"key":"ref_66","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the ICML, Virtual."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Qiu, L., Zhang, Q., Chen, X., and Cai, S. (2024, January 26\u201327). Multi-Level Cross-Modal Alignment for Image Clustering. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i13.29387"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/12\/1200\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T05:33:26Z","timestamp":1764308006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/12\/1200"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":67,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["e27121200"],"URL":"https:\/\/doi.org\/10.3390\/e27121200","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2025,11,26]]}}}