Vector Quantized Generative Adversarial Network

Vector-Quantized Generative Adversarial Networks (VQGANs) combine the power of GANs with vector quantization to generate high-quality data from discrete latent representations. Current research focuses on improving VQGAN performance in diverse applications, including image and speech synthesis, medical image augmentation, and anomaly detection, often employing architectures like VQ-VAEs and incorporating techniques such as latent diffusion and self-supervised learning. This approach offers advantages in terms of speed, data efficiency, and the ability to generate diverse outputs, impacting fields ranging from medical imaging to speech processing and industrial quality control. The ability to efficiently generate realistic data is particularly valuable for addressing data imbalance issues in machine learning.