Once a generative AI algorithm undergoes training, it gains the ability to produce novel outputs that closely resemble the data it was trained on. However, it’s important to note that Generative AI typically demands more computational power than discriminative AI, making it a potentially costlier choice for implementation.
Among the most commonly employed generative models for text and image generation are Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
In a GAN, a dual machine learning model setup is utilized. One component is known as the generator, and the other is the discriminator. The generator’s task is to craft new outputs that exhibit similarities to the training data. On the other hand, the discriminator’s role is to evaluate the generated data and provide feedback to the generator, helping it refine its output.
In the case of a VAE, a single machine learning model is trained to encode data into a lower-dimensional representation that encapsulates the essential characteristics, structure, and relationships of the data in a more compact form. Subsequently, the model deciphers this low-dimensional representation back into the original data. Essentially, this encoding and decoding process empowers the model to acquire a concise understanding of the data distribution, which it can then leverage to generate fresh outputs.