The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Complex-Valued Neural Networks: A Comprehensive Survey
Abstract
Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared to their real counter-parts in speech enhancement, image and signal processing. Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs. Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals, this area of study will grow and expect the arrival of some effective improvements in the future. Therefore, there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs. In this paper, we discuss and summarize the recent advances based on their learning algorithms, activation functions, which is the most challenging part of building a CVNN, and applications. Besides, we outline the structure and applications of complex-valued convolutional, residual and recurrent neural networks. Finally, we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.
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Citations
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861
Amin, M. F., Amin, M. I., Al-Nuaimi, A. Y. H., & Murase, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24955-6_66 · Cited by 65
Lv, M., Xu, W., & Chen, T. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.051 · Cited by 64
Metadata
- Authors
- Lee, C., Hasegawa, H., & Gao, S.
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2022-08
- Volume
- 9
- Issue
- 8
- Pages
- 1406-1426
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W4289654500
- Cited by
- 289
- References
- 8
- FWCI
- 16.96
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lee, C., Hasegawa, H., & Gao, S. (2022). Complex-Valued Neural Networks: A Comprehensive Survey. IEEE/CAA Journal of Automatica Sinica. https://doi.org/10.1109/jas.2022.105743
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