Replication Data for: Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
收藏DataCite Commons2025-10-09 更新2026-05-03 收录
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https://rdr.kuleuven.be/citation?persistentId=doi:10.48804/K6W4ZB
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资源简介：
In the paper:
"Aghabagherloo, Alireza, Aydin Abadi, Sumanta Sarkar, Vishnu Asutosh Dasu, and Bart Preneel. 'Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models.' 2025 IEEE Security and Privacy Workshops (SPW), pp. 177-183. IEEE, 2025,"
We evaluated the effect of data duplication on standard and adversarially trained models using the widely used benchmark CIFAR-10 dataset. CIFAR-10 contains 60,000 color images across 10 classes for object recognition tasks. Duplicated data was randomly selected from CIFAR-10 and repeated during training. We also explored the effect of duplicating data points selected from a Gaussian distribution.
提供机构：
KU Leuven RDR
创建时间：
2025-09-17
