Regarding training of classifier

[adithyaiyer1999]

Hi! Thank you for such readable code - I had one question wrt the training process.

In Line 322, and Line 327 of train_util.py (the forward backward function), you select 2 sets of time-steps one with t =0, and one with t sampled from the diffusion steps. You also correctly associate both of these to the clean image and noisy image respectively.

My qs is : Why are you using 2 separate sets of images (a clean set, and noisy set), and concatenating them to train the classifier on the batch?

Thanks in advance!

[GuoQiushan]

Thanks for your interest in our work. We use the clean set for a better classification performance, since the noisy set contribute less to the clean classification performance.

[adithyaiyer1999]

Thanks for your reply, that makes sense. I had another qs along the same line.

In eq 10
$\nabla_{x_t} \log q(x_t | x_0) = - \frac{\epsilon_t}{\sqrt{1 - \bar{\alpha}_t}}$

i.e., the score is related to epsilon with a scaling factor of $\sqrt{1 - \bar{\alpha}_t}$.

When you create the denoising training loss (i.e. fisher divergence, in Algorithm 1 in the paper, you add the score from $\epsilon$. I was wondering, would we not have to multiply score by the scaling factor and then get the MSE ?

Thanks again for the clarifications!