Generative Adversarial Networks: Applications in Pattern Analysis

Generative Adversarial Networks (GANs) are a powerful class of deep generative models that learn data distributions through an adversarial game between a generator and a discriminator [۱]. Since their introduction, GANs have enabled realistic sample generation and have had a major impact on pattern analysis tasks such as data augmentation, anomaly detection, domain adaptation, image translation, and simulation [۲]–[۴]. This brief report reviews the core GAN formulation, summarizes the main training difficulties, and highlights practical stabilization techniques and representative applications in pattern analysis. In particular, methods such as Wasserstein objectives, gradient penalties, and spectral normalization have improved GAN training stability and synthesis quality [۵]–[۷]. Despite these advances, challenges including mode collapse, instability, controllability, and evaluation ambiguity remain open research problems [۱], [۵], [۷]. These issues continue to shape the development of GAN-based approaches for real-world pattern analysis systems.

کلیدواژه ها:

Generative Adversarial Networks ، Adversarial Learning ، Pattern Analysis ، Data Augmentation ، Anomaly Detection ، Domain Adaptation.