An Approach to Finding a Robust Deep Learning Model
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of a large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. In response...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11030460/ |
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| _version_ | 1849689684767145984 |
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| author | Alexey Boldyrev Fedor Ratnikov Andrey Shevelev |
| author_facet | Alexey Boldyrev Fedor Ratnikov Andrey Shevelev |
| author_sort | Alexey Boldyrev |
| collection | DOAJ |
| description | The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of a large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. In response to this need, we propose a novel approach for determining model robustness. This approach, supplemented with a model selection algorithm designed as a meta-algorithm, is versatile and applicable to any machine learning model, provided that it is appropriate for the task at hand. This study demonstrates the application of our approach to evaluate the robustness of deep learning models. To this end, we study small models composed of a few convolutional and fully connected layers, using common optimizers because of their ease of interpretation and computational efficiency. We address the influence of training sample size, model weight initialization, and inductive bias on the robustness of deep learning models. |
| format | Article |
| id | doaj-art-9bcdb50d9f89431a8d99391d46e369cb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9bcdb50d9f89431a8d99391d46e369cb2025-08-20T03:21:32ZengIEEEIEEE Access2169-35362025-01-011310239010240610.1109/ACCESS.2025.357892611030460An Approach to Finding a Robust Deep Learning ModelAlexey Boldyrev0https://orcid.org/0000-0002-7872-6819Fedor Ratnikov1Andrey Shevelev2Laboratory of Methods for Big Data Analysis, HSE University, Moscow, RussiaLaboratory of Methods for Big Data Analysis, HSE University, Moscow, RussiaLaboratory of Methods for Big Data Analysis, HSE University, Moscow, RussiaThe rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of a large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. In response to this need, we propose a novel approach for determining model robustness. This approach, supplemented with a model selection algorithm designed as a meta-algorithm, is versatile and applicable to any machine learning model, provided that it is appropriate for the task at hand. This study demonstrates the application of our approach to evaluate the robustness of deep learning models. To this end, we study small models composed of a few convolutional and fully connected layers, using common optimizers because of their ease of interpretation and computational efficiency. We address the influence of training sample size, model weight initialization, and inductive bias on the robustness of deep learning models.https://ieeexplore.ieee.org/document/11030460/Machine learningdeep learningconvolutional neural networksmodel selectionmodel robustnessinductive bias |
| spellingShingle | Alexey Boldyrev Fedor Ratnikov Andrey Shevelev An Approach to Finding a Robust Deep Learning Model IEEE Access Machine learning deep learning convolutional neural networks model selection model robustness inductive bias |
| title | An Approach to Finding a Robust Deep Learning Model |
| title_full | An Approach to Finding a Robust Deep Learning Model |
| title_fullStr | An Approach to Finding a Robust Deep Learning Model |
| title_full_unstemmed | An Approach to Finding a Robust Deep Learning Model |
| title_short | An Approach to Finding a Robust Deep Learning Model |
| title_sort | approach to finding a robust deep learning model |
| topic | Machine learning deep learning convolutional neural networks model selection model robustness inductive bias |
| url | https://ieeexplore.ieee.org/document/11030460/ |
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