🔑Discrete data, such as text or DNA sequences, poses unique challenges for generative models designed for continuous data.
💡Existing continuous models, like flows or GANs, struggle to handle discrete data effectively due to limitations in calculus and backpropagation.
⚙️Discretizing continuous generative models is a two-step process that involves generating a continuous image first and then discretizing it.
🌐Embedding tokens into continuous space is not a viable solution for generating discrete data, as it results in sparse distributions and empty spaces between tokens.
🔬Further research is needed to develop effective generative models specifically designed for discrete data, taking into account the unique properties and challenges it presents.