The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep Residual Learning for Image Recognition: A Survey
Open accessAbstract
Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.
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Deep Residual Learning for Image Recognition
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Xception: Deep Learning with Depthwise Separable Convolutions
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
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Adaptive batch normalization for practical domain adaptation
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Deep residual learning for image steganalysis
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Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
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Unsupervised Pretraining Transfers Well Across Languages
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Cited by
Raza, S. M., Abidi, S. M. H., Masuduzzaman, M., & Shin, S. Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131357 · Cited by 13
Metadata
- Authors
- Shafiq, M., & Gu, Z.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-09-07
- Volume
- 12
- Issue
- 18
- Pages
- 8972
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4295122555
- Cited by
- 1,023
- References
- 9
- FWCI
- 64.67
- Type
- journal article
- Language
- en
- Open access
- gold
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