some doubts about the principle of the diffusion model about anoddpm

[Julian-Wyatt]

You've pretty much answered your own question.

This is a common method for unsupervised anomaly detection. Where we have a generator which is trained to generate healthy images. So when we encode a new unlabelled image; in theory if it was anomalous it would be decoded such that the model "imagines" what it would look like if it was healthy. Then we can deduce areas of high reconstruction error are consequently anomalous.
So yes, in short, it is because the "encoding" by the forward process learns what noise is in healthy images, but when reversing this process any anomaly would be considered as part of this noise and removed in the denoising process.