How do GANs differ from explicit generative models in terms of learning the data distribution and generating new samples?
Tuesday, 11 June 2024
by EITCA Academy
Generative models are a class of machine learning frameworks that aim to generate new data samples from an underlying data distribution. These models are important for various applications, including image synthesis, text generation, and data augmentation. Among generative models, Generative Adversarial Networks (GANs) have emerged as a powerful and popular approach. However, GANs differ significantly

