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|a 10.1109/TPAMI.2023.3265198
|2 doi
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|a eng
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| 100 |
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|a Chen, Bohong
|e verfasserin
|4 aut
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|a Prioritized Subnet Sampling for Resource-Adaptive Supernet Training
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 07.08.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a A resource-adaptive supernet adjusts its subnets for inference to fit the dynamically available resources. In this paper, we propose prioritized subnet sampling to train a resource-adaptive supernet, termed PSS-Net. We maintain multiple subnet pools, each of which stores the information of substantial subnets with similar resource consumption. Considering a resource constraint, subnets conditioned on this resource constraint are sampled from a pre-defined subnet structure space and high-quality ones will be inserted into the corresponding subnet pool. Then, the sampling will gradually be prone to sampling subnets from the subnet pools. Moreover, the one with a better performance metric is assigned with higher priority to train our PSS-Net, if sampling is from a subnet pool. At the end of training, our PSS-Net retains the best subnet in each pool to entitle a fast switch of high-quality subnets for inference when the available resources vary. Experiments on ImageNet using MobileNet-V1/V2 and ResNet-50 show that our PSS-Net can well outperform state-of-the-art resource-adaptive supernets. Our project is publicly available at https://github.com/chenbong/PSS-Net
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|a Journal Article
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| 700 |
1 |
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|a Lin, Mingbao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Rongrong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Liujuan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 9 vom: 17. Sept., Seite 11108-11119
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:45
|g year:2023
|g number:9
|g day:17
|g month:09
|g pages:11108-11119
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|u http://dx.doi.org/10.1109/TPAMI.2023.3265198
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