Paper Contents
Abstract
Data Augmentation using Generative Adversarial Networks has emerged as crucial technique in enhancing the performance of Machine Learning models. When dealing with datasets, GANs generate realistic synthetic data that can improve model generalization, outperforming traditional augmented methods like cropping and noise addition. Study has been especially effective in complex domains such as object detection, human activity recognition, signal processing, ESP fault diagnosis, chatbot training. GAN based data augmentation offer advantages such as improved data diversity and reduced need for costly data collection. Challenges, including training instability, mode collapse, potential introduction of poor-quality data. Overcoming of these challenges, advanced GAN architecture like condition GANs(cGANs), Wasserstein GANs(WGANs) and Regularization technique has been developed. Architecture has enhanced training stability and data quality by incorporating technique such as 1D convolution and adaptive discriminator augmentation. Application of GAN-augmentation has been extended in various fields, including automative, healthcare, education, E-commerce, finance, telecommunication. GAN-Based Data augmentation remains a powerful tool for enhancing AI driven solution in data-scare environment.
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Copyright © 2024 Satti sandeep reddy. This is an open access article distributed under the Creative Commons Attribution License.
