Generative Adversarial Networks (GANs): Applications in Image Synthesis, Anomaly Detection, and Data Augmentation
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Abstract
Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating synthetic data with applications in various fields, including image synthesis, anomaly detection, and data augmentation. the versatility and effectiveness of GANs in these domains. GANs consist of two neural networks, the generator and the discriminator, trained simultaneously in a competitive setting. The generator learns to produce realistic samples from random noise, while the discriminator learns to distinguish between real and fake samples. Through adversarial training, GANs can generate high-quality, diverse, and realistic data that closely resemble the training distribution.
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