Abstract:
Recently, the application of advanced AI algorithms represented by deep neural network (DNN) in various fields of human society has shown explosive growth. These powerful...Show MoreMetadata
Abstract:
Recently, the application of advanced AI algorithms represented by deep neural network (DNN) in various fields of human society has shown explosive growth. These powerful AI tools have not only revolutionized the traditional working mode of the technology industry, but also deeply penetrated into people’s daily life. However, since AI algorithms generally have problems such as uncertain capability boundaries and difficult interpretability, failing to adequately and accurately measure their quality will bring about great hidden dangers. Traditional software testing and evaluation methods can hardly meet the need of comprehensively measuring the quality of DNN models. To solve this problem, we propose a testing and evaluation framework for the quality of DNN models. Experiments demonstrate that our proposed framework can comprehensively test and evaluate the quality of DNN models, as well as improve the adequacy and accuracy of testing and evaluation through dataset quality analysis and test adequacy analysis.
Date of Conference: 16-17 March 2024
Date Added to IEEE Xplore: 21 June 2024
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ISSN Information:
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- IEEE Keywords
- Index Terms
- Deep Neural Network ,
- Model Quality ,
- Deep Neural Network Model ,
- Testing Framework ,
- Neural Network ,
- Evaluation Method ,
- Test Accuracy ,
- Quality Analysis ,
- People’s Daily ,
- Software Testing ,
- Difficult Interpretation ,
- Traditional Evaluation Methods ,
- Model Performance ,
- Training Dataset ,
- Image Features ,
- Test Dataset ,
- Effect Test ,
- Comprehensive Evaluation ,
- Ability Of The Model ,
- Real-world Applications ,
- Adversarial Examples ,
- Test Subjects ,
- Test Plan ,
- Image Augmentation ,
- Field Of Object Detection ,
- Quality Characteristics ,
- Erroneous Data ,
- Evaluation Phase ,
- Executive Tests ,
- Pose Estimation
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Neural Network ,
- Model Quality ,
- Deep Neural Network Model ,
- Testing Framework ,
- Neural Network ,
- Evaluation Method ,
- Test Accuracy ,
- Quality Analysis ,
- People’s Daily ,
- Software Testing ,
- Difficult Interpretation ,
- Traditional Evaluation Methods ,
- Model Performance ,
- Training Dataset ,
- Image Features ,
- Test Dataset ,
- Effect Test ,
- Comprehensive Evaluation ,
- Ability Of The Model ,
- Real-world Applications ,
- Adversarial Examples ,
- Test Subjects ,
- Test Plan ,
- Image Augmentation ,
- Field Of Object Detection ,
- Quality Characteristics ,
- Erroneous Data ,
- Evaluation Phase ,
- Executive Tests ,
- Pose Estimation
- Author Keywords