EEEA-Net: An Early Exit Evolutionary Neural Architecture Search
The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable for an on-device processor with limited computing resources, performing at substantially lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population Initialis...
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| Veröffentlicht in: | Engineering applications of artificial intelligence Jg. 104; S. 104397 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.09.2021
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable for an on-device processor with limited computing resources, performing at substantially lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI) for Evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI reduces the total number of parameters in the search process by filtering the models with fewer parameters than the maximum threshold. It will look for a new model to replace those models with parameters more than the threshold. Thereby, reducing the number of parameters, memory usage for model storage and processing time while maintaining the same performance or accuracy. The search time was reduced to 0.52 GPU day. This is a huge and significant achievement compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early Exit Evolutionary Algorithm networks (EEEA-Nets) yield network architectures with minimal error and computational cost suitable for a given dataset as a class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02% for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this image recognition architecture for other tasks, such as object detection, semantic segmentation, and keypoint detection tasks, and, in our experiments, EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The algorithm code is available at https://github.com/chakkritte/EEEA-Net). |
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| AbstractList | The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable for an on-device processor with limited computing resources, performing at substantially lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI) for Evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI reduces the total number of parameters in the search process by filtering the models with fewer parameters than the maximum threshold. It will look for a new model to replace those models with parameters more than the threshold. Thereby, reducing the number of parameters, memory usage for model storage and processing time while maintaining the same performance or accuracy. The search time was reduced to 0.52 GPU day. This is a huge and significant achievement compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early Exit Evolutionary Algorithm networks (EEEA-Nets) yield network architectures with minimal error and computational cost suitable for a given dataset as a class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02% for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this image recognition architecture for other tasks, such as object detection, semantic segmentation, and keypoint detection tasks, and, in our experiments, EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The algorithm code is available at https://github.com/chakkritte/EEEA-Net). |
| ArticleNumber | 104397 |
| Author | Muneesawang, Paisarn Ieamsaard, Jirarat Lee, Ivan Termritthikun, Chakkrit Jamtsho, Yeshi |
| Author_xml | – sequence: 1 givenname: Chakkrit orcidid: 0000-0002-1508-3123 surname: Termritthikun fullname: Termritthikun, Chakkrit email: chakkritt60@email.nu.ac.th organization: STEM, University of South Australia, Adelaide, SA, 5095, Australia – sequence: 2 givenname: Yeshi surname: Jamtsho fullname: Jamtsho, Yeshi email: yjamtsho.cst@rub.edu.bt organization: College of Science and Technology, Royal University of Bhutan, Phuentsholing, 21101, Bhutan – sequence: 3 givenname: Jirarat surname: Ieamsaard fullname: Ieamsaard, Jirarat email: jirarati@nu.ac.th organization: Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, 65000, Thailand – sequence: 4 givenname: Paisarn surname: Muneesawang fullname: Muneesawang, Paisarn email: paisarnmu@nu.ac.th organization: Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, 65000, Thailand – sequence: 5 givenname: Ivan orcidid: 0000-0002-2826-6367 surname: Lee fullname: Lee, Ivan email: ivan.lee@unisa.edu.au organization: STEM, University of South Australia, Adelaide, SA, 5095, Australia |
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| Cites_doi | 10.1145/3321707.3321729 10.1023/A:1022672621406 10.1016/j.neucom.2017.12.049 10.1007/s11263-015-0816-y 10.1016/j.engappai.2017.05.005 10.1016/j.engappai.2019.07.018 10.1007/s11042-019-08332-3 10.1007/978-3-030-01261-8_20 10.1109/4235.996017 10.1007/978-3-030-01264-9_8 10.1145/3065386 10.1007/978-3-030-01246-5_2 10.1007/s11263-009-0275-4 10.1609/aaai.v33i01.33014780 10.1016/j.engappai.2019.08.014 10.1007/978-3-030-01231-1_29 |
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