diffusioonilinely
Diffusionlinien, sometimes referred to as diffusion lines, are a concept that arises in statistical physics and machine learning, particularly in the context of diffusion models. These models are generative algorithms designed to create new data samples that resemble a training dataset. The core idea involves a gradual process of adding noise to data (forward diffusion) and then learning to reverse this process to generate new data (reverse diffusion).
The term "diffusionlinien" points to the sequence of states a data point or a distribution of data
In the context of training a diffusion model, the goal of the reverse diffusion process is to