The DOI matches its Crossref record; 278 of the 280 references it cites were found in a registry or an index.
Dynamic Neural Networks: A Survey
Open accessAbstract
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynamic networks can adapt their structures or parameters to different inputs, leading to notable advantages in terms of accuracy, computational efficiency, adaptiveness, etc. In this survey, we comprehensively review this rapidly developing area by dividing dynamic networks into three main categories: 1) sample-wise dynamic models that process each sample with data-dependent architectures or parameters; 2) spatial-wise dynamic networks that conduct adaptive computation with respect to different spatial locations of image data; and 3) temporal-wise dynamic models that perform adaptive inference along the temporal dimension for sequential data such as videos and texts. The important research problems of dynamic networks, e.g., architecture design, decision making scheme, optimization technique and applications, are reviewed systematically. Finally, we discuss the open problems in this field together with interesting future research directions.
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In the citing paper
“In contrast, DNNs enhance expressive ability by dynamically adjusting the network depth or activating certain specific modules as needed.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [130]
In the citing paper
“DNNs[130] offer a potential solution, advancing the research of SOC estimation through their abilities to adaptively adjust their structure and parameters based on input data during the inference phase, providing greater flexibility than traditional static networks.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [130]
In the citing paper
“DNNs, however, allocate computational resources efficiently by activating specific modules as needed.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [130]
In the citing paper
“In contrast, static networks maintain fixed configurations post-training, which may be suboptimal in practical applications due to limited representational ability and inflexible resource allocation.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [130]
In the citing paper
“DNNs are compatible with existing NN technologies and can seamlessly incorporate various advanced DL technologies.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [130]
Citations
Cites
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Cited by
Sorted by citations
SimMIM: a Simple Framework for Masked Image Modeling
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., & Hu, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00943 · Cited by 1,495
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
Nonlinear Transform Source-Channel Coding for Semantic Communications
Dai, J., Wang, S., Tan, K., Si, Z., Qin, X., Niu, K., & Zhang, P. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3180802 · Cited by 395
FLatten Transformer: Vision Transformer using Focused Linear Attention
Han, D., Pan, X., Han, Y., Song, S., & Huang, G. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00548 · Cited by 386
Self-citation
Metadata
- Authors
- Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y.
- Affiliations
- Tsinghua University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-11-01
- Volume
- 44
- Issue
- 11
- Pages
- 7436-7456
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3204647170
- Cited by
- 848
- References
- 280
- Citations by year
- 2026: 162 · 2025: 213 · 2024: 191 · 2023: 170 · 2022: 74
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science and Technology Major Project (2018AAA0100701)
- National Natural Science Foundation of China (61906106, 62022048)
- Tsinghua University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- dynamic neural networks, adaptive computation, adaptive inference, architectural design, decision-making scheme, optimization methods, sequential data, video analysis, text analysis
- MeSH terms
- Algorithms, Neural Networks, Computer
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