Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

The study of time series is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …

This paper introduces $\infty $-Diff, a generative diffusion model defined in an infinite-
dimensional Hilbert space, which can model infinite resolution data. By training on randomly …

Diffusion models (DMs) are a type of potential generative models, which have achieved
better effects in many fields than traditional methods. DMs consist of two main processes …

We propose a new class of generative model that naturally handles data of varying
dimensionality by jointly modeling the state and dimension of each datapoint. The …

Diffusion models, which employ stochastic differential equations to sample images through
integrals, have emerged as a dominant class of generative models. However, the rationality …

Generative diffusion models showed high success in many fields with a powerful theoretical
background. They convert the data distribution to noise and remove the noise back to obtain …

Electrical impedance tomography (EIT) is a non-invasive imaging technique, which has
been widely used in the fields of industrial inspection, medical monitoring and tactile …

Diffusion Model (DM) is a hot topic in deep generative models, and it is widely applied in the
image generation fields. In diffusion models, there are 4 main “secrets” that affect the …