Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915870283923456 |
|---|---|
| author | Aghabagherloo, Alireza Abadi, Aydin Sarkar, Sumanta Dasu, Vishnu Asutosh Preneel, Bart |
| author_facet | Aghabagherloo, Alireza Abadi, Aydin Sarkar, Sumanta Dasu, Vishnu Asutosh Preneel, Bart |
| contents | The accuracy and robustness of machine learning models against adversarial attacks are significantly influenced by factors such as training data quality, model architecture, the training process, and the deployment environment. In recent years, duplicated data in training sets, especially in language models, has attracted considerable attention. It has been shown that deduplication enhances both training performance and model accuracy in language models. While the importance of data quality in training image classifier Deep Neural Networks (DNNs) is widely recognized, the impact of duplicated images in the training set on model generalization and performance has received little attention.
In this paper, we address this gap and provide a comprehensive study on the effect of duplicates in image classification. Our analysis indicates that the presence of duplicated images in the training set not only negatively affects the efficiency of model training but also may result in lower accuracy of the image classifier. This negative impact of duplication on accuracy is particularly evident when duplicated data is non-uniform across classes or when duplication, whether uniform or non-uniform, occurs in the training set of an adversarially trained model. Even when duplicated samples are selected in a uniform way, increasing the amount of duplication does not lead to a significant improvement in accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models Aghabagherloo, Alireza Abadi, Aydin Sarkar, Sumanta Dasu, Vishnu Asutosh Preneel, Bart Machine Learning Artificial Intelligence Image and Video Processing The accuracy and robustness of machine learning models against adversarial attacks are significantly influenced by factors such as training data quality, model architecture, the training process, and the deployment environment. In recent years, duplicated data in training sets, especially in language models, has attracted considerable attention. It has been shown that deduplication enhances both training performance and model accuracy in language models. While the importance of data quality in training image classifier Deep Neural Networks (DNNs) is widely recognized, the impact of duplicated images in the training set on model generalization and performance has received little attention. In this paper, we address this gap and provide a comprehensive study on the effect of duplicates in image classification. Our analysis indicates that the presence of duplicated images in the training set not only negatively affects the efficiency of model training but also may result in lower accuracy of the image classifier. This negative impact of duplication on accuracy is particularly evident when duplicated data is non-uniform across classes or when duplication, whether uniform or non-uniform, occurs in the training set of an adversarially trained model. Even when duplicated samples are selected in a uniform way, increasing the amount of duplication does not lead to a significant improvement in accuracy. |
| title | Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models |
| topic | Machine Learning Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2504.00638 |