Generative deep learning refers to the use of deep learning architectures to generate new data that is similar to some existing data. These models can create realistic, synthetic examples of various types of data, such as images, text, and audio.
Generative deep learning models, like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), work by capturing complex patterns within the data they are trained on. GANs consist of two networks: a generator that creates new data, and a discriminator that tries to differentiate between real and generated data. The two networks are trained in competition with one another, refining their capabilities in a kind of adversarial game.
VAEs, on the other hand, use a probabilistic approach to model the underlying distribution of the data, allowing for the generation of new samples from that distribution.
Generative deep learning has applications in various fields, including art, healthcare, entertainment, and more. It can be used to create realistic images or modify existing ones, synthesize music, design new pharmaceutical compounds, and even augment datasets to improve the performance of other machine learning models.