Generative Adversarial Network

Generative Adversarial Networks (GANs) are a class of deep learning models designed to generate new data instances that resemble a training dataset. Current research focuses on improving GAN training stability, enhancing the quality and diversity of generated data, and applying GANs to diverse fields like medical imaging, drug discovery, and time series analysis, often incorporating techniques like contrastive learning and disentangled representation learning to improve model performance and interpretability. The ability of GANs to synthesize realistic data addresses critical limitations in data availability and annotation costs across numerous scientific disciplines and practical applications, leading to advancements in areas ranging from medical diagnosis to robotic control.