Architecture of Generative AI: Exploring the Models and Techniques for Creative Content Generation
Generative AI refers to the field of artificial intelligence that focuses on creating systems and models capable of generating new and original content, such as images, text, music, and more. The architecture of generative AI enterprise typically involves the use of deep learning models, specifically generative models, which aim to learn the underlying patterns and structures in a given dataset to generate new samples that resemble the training data. There are several popular architectures used in generative AI, including: Variational Autoencoders (VAEs): VAEs are probabilistic generative models that consist of an encoder and a decoder. The encoder maps input data to a latent space, while the decoder reconstructs the data from the latent space back to the original input space. VAEs are trained using techniques such as the variational inference and the reparameterization trick. Generative Adversarial Networks (GANs): GANs are composed of two neural networks—a generator and a discrimin...