What distinguishes diffusion models in AI?

Diffusion models in AI are characterized by the process of progressively adding noise to data, and then learning to reverse this process to generate new samples, such as images. This method begins with a data distribution and systematically introduces noise over time, resulting in a corrupted version of the data. The model is trained to learn how to gradually remove this noise during the generation phase, effectively creating new samples that have similar properties to the original data.

By leveraging this approach, diffusion models can produce high-quality images that reflect learned patterns and features from the training dataset. This capability is what sets them apart from many other generative models, which may not use noise or work in a similar stochastic manner. Thus, option B accurately captures the essence of how diffusion models operate within the realm of artificial intelligence.