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What is Adversarial Data Augmentation for Equipment about?

This paper proposes a data augmentation method using generative adversarial networks (GANs) combined with adversarial training for classifying construction equipment images. The method aims to address the challenge of acquiring sufficient labeled data for deep learning by generating adversarial samples, which improves the performance of classifiers. Experimental results demonstrated an average accuracy improvement of 4.094% in classifying excavator images, showcasing the robustness of the proposed approach.

Author

the.monkas

Language

EN