Generative Models in AI: A Study of GANs and Variational Autoencoders

Abstract

Machines can now learn underlying data distributions and produce fresh, realistic data samples thanks to generative models, which have been a major focus of AI research. Image synthesis, data augmentation, anomaly detection, and content development are just a few of the many applications where Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have proven to be highly effective. These models illustrate two different approaches to generative learning that work well together. a thorough examination of GANs and VAEs, with an emphasis on their designs, methods of learning, and performance attributes. To function, GANs employ an adversarial framework that comprises a discriminator network and a generator network. The discriminator network's job is to differentiate between actual and created data, while the generator network tries to create data samples that seem realistic. Even though it produces high-quality results, this competitive approach is not immune to problems like mode collapse and training instability. Contrarily, VAEs use an encoder-decoder structure to learn latent representations of data, embracing a probabilistic approach. Although VAEs offer consistent training and significant latent areas, their outputs might not be as crisp as those of GANs.