1 code implementation • CVPR 2023 • Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward process.
no code implementations • 22 Apr 2023 • Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE).
no code implementations • 9 Dec 2021 • Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
Aida is designed to compute the qth power of the magnitude in the form of |m_{t+1}|^q/(r_{t+1}+epsilon)^(q/p) (or |m_{t+1}|^q/((r_{t+1})^(q/p)+epsilon)), which reduces to that of AdamW when (p, q)=(2, 1).
