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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.15332 |
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| _version_ | 1866917590672080896 |
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| author | Dai, Wei Berleant, Daniel |
| author_facet | Dai, Wei Berleant, Daniel |
| contents | In the context of deep learning research, where model introductions continually occur, the need for effective and efficient evaluation remains paramount. Existing methods often emphasize accuracy metrics, overlooking stability. To address this, the paper introduces the Accuracy-Stability Index (ASI), a quantitative measure incorporating both accuracy and stability for assessing deep learning models. Experimental results demonstrate the application of ASI, and a 3D surface model is presented for visualizing ASI, mean accuracy, and coefficient of variation. This paper addresses the important issue of quantitative benchmarking metrics for deep learning models, providing a new approach for accurately evaluating accuracy and stability of deep learning models. The paper concludes with discussions on potential weaknesses and outlines future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_15332 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ASI: Accuracy-Stability Index for Evaluating Deep Learning Models Dai, Wei Berleant, Daniel Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Information Theory Performance In the context of deep learning research, where model introductions continually occur, the need for effective and efficient evaluation remains paramount. Existing methods often emphasize accuracy metrics, overlooking stability. To address this, the paper introduces the Accuracy-Stability Index (ASI), a quantitative measure incorporating both accuracy and stability for assessing deep learning models. Experimental results demonstrate the application of ASI, and a 3D surface model is presented for visualizing ASI, mean accuracy, and coefficient of variation. This paper addresses the important issue of quantitative benchmarking metrics for deep learning models, providing a new approach for accurately evaluating accuracy and stability of deep learning models. The paper concludes with discussions on potential weaknesses and outlines future research directions. |
| title | ASI: Accuracy-Stability Index for Evaluating Deep Learning Models |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Information Theory Performance |
| url | https://arxiv.org/abs/2311.15332 |