Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures

Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures

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 input distribution whil...

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Bibliographic Details
Main Authors: Lucas C. Ribas, Wallace Casaca, Ricardo T. Fares
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:AI
Subjects:
conditional generative adversarial networks
data augmentation
deep learning
deep neural networks
image processing
Online Access:https://www.mdpi.com/2673-2688/6/2/32

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https://www.mdpi.com/2673-2688/6/2/32

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