This characteristic enables simulation-free training, bypassing the necessity to simulate the underlying diffusion processes by directly sampling the diffused data.

Advances in in computer vision have lead to classifiers that nearly match human performance in many applications. However, while the human visual system is remarkably versatile in extracting semantic meaning out of even degraded and heavily obfuscated images, today's visual classifiers significantly lag behind in emulating the same robustness, and often yield incorrect outputs in the presence of natural and adversarial degradations.

Temporal context plays a significant role in temporal action segmentation. In an offline setting, the context is typically captured by the segmentation network after observing the entire sequence. However, capturing and using such context information in an online setting remains an under-explored problem.

But, they are limited to unimodal, Gaussian approximations of state uncertainty.