MGE: A Training-Free and Efficient Model Generation and Enhancement Scheme
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917599375261696 |
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| author | Wang, Xuan Pang, Zeshan Lu, Yuliang Yan, Xuehu |
| author_facet | Wang, Xuan Pang, Zeshan Lu, Yuliang Yan, Xuehu |
| contents | To provide a foundation for the research of deep learning models, the construction of model pool is an essential step. This paper proposes a Training-Free and Efficient Model Generation and Enhancement Scheme (MGE). This scheme primarily considers two aspects during the model generation process: the distribution of model parameters and model performance. Experiments result shows that generated models are comparable to models obtained through normal training, and even superior in some cases. Moreover, the time consumed in generating models accounts for only 1\% of the time required for normal model training. More importantly, with the enhancement of Evolution-MGE, generated models exhibits competitive generalization ability in few-shot tasks. And the behavioral dissimilarity of generated models has the potential of adversarial defense. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17486 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MGE: A Training-Free and Efficient Model Generation and Enhancement Scheme Wang, Xuan Pang, Zeshan Lu, Yuliang Yan, Xuehu Computer Vision and Pattern Recognition To provide a foundation for the research of deep learning models, the construction of model pool is an essential step. This paper proposes a Training-Free and Efficient Model Generation and Enhancement Scheme (MGE). This scheme primarily considers two aspects during the model generation process: the distribution of model parameters and model performance. Experiments result shows that generated models are comparable to models obtained through normal training, and even superior in some cases. Moreover, the time consumed in generating models accounts for only 1\% of the time required for normal model training. More importantly, with the enhancement of Evolution-MGE, generated models exhibits competitive generalization ability in few-shot tasks. And the behavioral dissimilarity of generated models has the potential of adversarial defense. |
| title | MGE: A Training-Free and Efficient Model Generation and Enhancement Scheme |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.17486 |