What occurs during the diffusion process in VAEs and U-nets?

During the diffusion process in Variational Autoencoders (VAEs) and U-nets, noise is iteratively added to the input. This process is essential to training these models, as it allows them to learn robust representations of data. In VAEs, for example, the diffusion process helps to sample from a distribution in the latent space by gradually introducing noise. This results in smoother transitions in the generated images and aids in capturing the underlying data distribution.

In U-nets, the diffusion process can also be viewed in the context of how noise is handled during the generation or reconstruction phases. By adding noise at different levels, the model can better understand and recreate complex features in the data.

This iterative addition of noise contrasts with other processes where immediate feedback is provided, the image is reconstructed directly from the input, or where optimization is specifically targeting feature maps without the gradual introduction of noise.