The details of model about noise_flow HOT 6 OPEN

Hi,

This is just a convention. You may simply flip the figure and call the training direction "inverse" and the sampling direction "forward" and nothing would change, that is, the internal operation in the layers would not change. Hope this helps!

from noise_flow.

The training process is to sample the noise distribution (the latent space like z in Glow) to the noisy image (the data space like x in Glow). The goal of this work is to get the noise distribution at last (which is the inference in Glow). Therefore, the inverse direction is used as training here which is different from Glow which use the data space to approximate latent space as training.
I think my understanding may be the same as you and if there is anything wrong, please let me know !

from noise_flow.

What's more, is there the pytorch implementation of Noise Flow?

from noise_flow.

Just to clarify, in the noise flow paper, Figure 3, and in the code:

• The training is in the inverse direction: noise distribution --> normal distribution.
• The sampling is in the forward direction: normal distribution --> noise distribution.
• There might be a confusion from the fact that the noise distribution is denoted as n in the paper; but denoted as x in the code. Also, the normal distribution is denoted as x_0 in the paper; but denoted as z in the code.
  So, at the end, I don't see a difference in the training/sampling directions compared to the Glow model.

from noise_flow.

What's more, is there the pytorch implementation of Noise Flow?

Not currently; I hope we can do it in the future.

from noise_flow.

In the Glow, data space distribution --> normal distribution which is the training in your comment uses the forward calculation however use the inverse calculation in Noise Flow code (such as _inverse_and_log_det_jacobian function of all layers).
I think it is the difference and ask why use the inverse calculation instead of forward.

from noise_flow.