crossrefAI2025-02-07Cited by 18
Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures
Lucas C. Ribas, Wallace Casaca, Ricardo T. Fares
Effectively training deep learning models relies heavily on large datasets, as insufficient instances can hinder model generalization. A simple yet effective way to address this is by applying modern deep learning augmentation methods, as they synthesize new data matching the inp…