The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Residual Networks of Residual Networks: Multilevel Residual Networks
Open accessAbstract
A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set.
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Cites
Deep Residual Learning for Image Recognition
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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
Self-citationResNet and its application to medical image processing: Research progress and challenges
Xu, W., Fu, Y.-L., & Zhu, D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107660 · Cited by 416
Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109531 · Cited by 310
Applications of transformer-based language models in bioinformatics: a survey
Zhang, S., Fan, R., Liu, Y., Chen, S., Liu, Q., & Zeng, W. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad001 · Cited by 176
Shobayo, O., & Saatchi, R. (2025) · Diagnostics · doi:10.3390/diagnostics15091072 · Cited by 53
Metadata
- Authors
- Zhang, K., Sun, M., Han, T. X., Yuan, X., Guo, L., & Liu, T.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2018-06
- Volume
- 28
- Issue
- 6
- Pages
- 1303-1314
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2498789492
- Cited by
- 401
- References
- 13
- FWCI
- 13.03
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, K., Sun, M., Han, T. X., Yuan, X., Guo, L., & Liu, T. (2018). Residual Networks of Residual Networks: Multilevel Residual Networks. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2017.2654543
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