Some question about equation (2) in your paper about diffusiondet

[ShoufaChen]

Hi,
Thank you for your feedback. I apologize for the delayed response due to the holiday season.

In our work, we employ the posterior distribution q(z_{t-1} | z_t, z_0) to train the prior distribution p(z_{t-1} | z_t). Our objective is to model the posterior distribution throughout the diffusion process. Our approach is different from data augmentation techniques in that it explicitly models the diffusion process.

[Tao-DoubleNine]

Thanks for your excellent and inspiring work, I also have a similar issue. Your loss function is related to z0 and the results of the model output, which is more like a process with only adding noise, because your network can directly predict z0, it seems unnecessary to infer and sample again.