While I've read a paper "Density Estimation using Real NVP", I have some confusing parts about multi-scale architecture in Section 3.6.
1. The author said that
We implement a multi-scale architecture using a squeezing operation: for each channel, it divides the image into subsquares of shape 2 × 2 × c, then reshapes them into subsquares of shape 1 × 1 × 4c. The squeezing operation transforms an s × s × c tensor into an s 2 × s 2 × 4c tensor, effectively trading spatial size for number of channels.
I don't exactly understand what's the relationship between "a squeezing operation" and "a multi-scale architecture". I think squeezing operation is just reshaping a size of an input image, but how it could be related with scaling?
2. The author implemented a multi-scale architecture by factoring out inputs whose are directly modeled as Gaussians like this.
The reason that the author implemented like this is that
Propagating a D dimensional vector through all the coupling layers would be cumbersome, in terms of computational and memory cost, and in terms of the number of parameters that would need to be trained.
Although above implementation have an advantage in cost, I think its performance become worse than propagating a D dimensional vector. I am wondering if there is another advantage of above implementation.
I'm very appreciated if you answer the questions. Thank you in advance.