The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture
Open accessAbstract
Clustering is a fundamental problem in many data-driven application domains, and clustering performance highly depends on the quality of data representation. Hence, linear or non-linear feature transformations have been extensively used to learn a better data representation for clustering. In recent years, a lot of works focused on using deep neural networks to learn a clustering-friendly representation, resulting in a significant increase of clustering performance. In this paper, we give a systematic survey of clustering with deep learning in views of architecture. Specifically, we first introduce the preliminary knowledge for better understanding of this field. Then, a taxonomy of clustering with deep learning is proposed and some representative methods are introduced. Finally, we propose some interesting future opportunities of clustering with deep learning and give some conclusion remarks.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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Cited by
A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246
An Anomaly Feature-Editing-Based Adversarial Network for Texture Defect Visual Inspection
Yang, H., Zhou, Q., Song, K., & Yin, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3015765 · Cited by 60
Unsupervised Human Activity Representation Learning with Multi-task Deep Clustering
Ma, H., Zhang, Z., Li, W., & Lu, S. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448074 · Cited by 48
Towards deep clustering of human activities from wearables
Abedin, A., Motlagh, F., Shi, Q., Rezatofighi, H., & Ranasinghe, D. (2020) · Proceedings of the 2020 International Symposium on Wearable Computers · doi:10.1145/3410531.3414312 · Cited by 27
Huang, L., & Lu, H. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1799 · Cited by 10
Metadata
- Authors
- Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., & Long, J.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 39501-39514
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2884851420
- Cited by
- 553
- References
- 7
- FWCI
- 32.11
- Type
- journal article
- Language
- en
- Open access
- gold
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