Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
This addresses a data quality issue for image classification practitioners, but it is incremental as it extends known effects from language models to image domains.
The study investigated how duplicated images in training sets affect deep neural network-based image classifiers, finding that duplication reduces training efficiency and accuracy, especially with non-uniform duplication or in adversarially trained models, and does not improve accuracy even with uniform duplication.
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.