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From Data Quality to Model Quality: An Exploratory Study on Deep Learning
Abstract
In the field of deep learning, people strive to construct high-quality deep neural networks (DNNs) to improve the accuracy of predicting. As well known, the quality of training data have great impacts on the quality of DNN models, since all the DNN models are obtained by training using these training data. However, there is not any reported systematic study on how the quality of training data affects the quality of DNN model. To study the relationships between data quality and model quality, we mainly consider four aspects of data quality including Skewed Classes, Sample Complexity, Label Quality, and Noisy Data in this paper. We design experiments on MNIST and Cifar-10, and attempt to find out the influences of four aspects on the quality of DNN models. Pearson correlation coefficient and Spearman correlation coefficient are utilized to evaluate such influences. Experimental results show that all the four aspects of data quality have significant impacts on the quality of DNN models. It means that the decrease of data quality in these four aspects will reduce the accuracy of the DNN models.
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The highly-discussed dimension target class balance pays tribute to the technical requirements of ML.
“Finally, the highly-discussed dimension target class balance pays tribute to the technical requirements of ML140,141,144,150,159.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [159]
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Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021) · Communications of the ACM · doi:10.1145/3446776 · Cited by 2,400
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The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review
Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01196-4 · Cited by 169
Metadata
- Authors
- He, T., Yu, S., Wang, Z., Li, J., & Chen, Z.
- Affiliations
- Nanjing University (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Venue
- Proceedings of the 11th Asia-Pacific Symposium on Internetware
- Publisher
- ACM
- Published
- 2019-10-28
- Pages
- 1-6
- OpenAlex
- W2981813488
- Cited by
- 24
- References
- 2
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 8 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Asia-Pacific Symposium on Internetware (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- data quality, model quality, deep neural network, class skew, sample complexity, label noise, noisy data, training data quality, model accuracy, MNIST, CIFAR-10
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