4 papers · 1 filter
Generative Modeling via Drifting
Mingyang Deng, He Li, Tianhong Li +2
Generative modeling can be formulated as learning a mapping f such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iter…
SparseDM: Toward Sparse Efficient Diffusion Models
Kafeng Wang, Jianfei Chen, He Li +2
Diffusion models represent a powerful family of generative models widely used for image and video generation. However, the time-consuming deployment, long inference time, and requi…
Solving Inverse Problems via Diffusion Optimal Control
Henry Li, Marcus Pereira
Existing approaches to diffusion-based inverse problem solvers frame the signal recovery task as a probabilistic sampling episode, where the solution is drawn from the desired post…
Non-Normal Diffusion Models
Henry Li
Diffusion models generate samples by incrementally reversing a process that turns data into noise. We show that when the step size goes to zero, the reversed process is invariant t…