From the perspective of diffusion model principle, why are the representations generated by DDPM from remote sensing images more robust and distinguishable than those obtained by UNet networks?

[ZhiyuLong0328]

Hello. Your article DDPM-CD has been very helpful to me, and I have a question I would like to discuss with you. The question is as follow.

From the perspective of diffusion model principle, why are the representations generated by DDPM from remote sensing images more robust and distinguishable than those obtained by UNet networks?

Looking forward to your reply, thank you.
Sincerely yours.

[jehovahxu]

I don't understand either. When T is large enough, the feature is approximate to gaussian noise. It is very different to use an auto-encoder such as UNet directly. So it is hard to understand why the image representations can be extracted from gaussion noise in the diffusion process.

[ZhiyuLong0328]

Can you understand why multi-time step features are more effective for change detection tasks than single-step feature? What additional information can it extract, and why are multiple time steps mutually reinforcing?