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Hi, I am confused about why do we scale the value of the x0 sample from x1 to [-1, 1].
I understand why when x is between (-1, 1), the log-likelihood would become L_{t-1} when t = 0.
But what about when x = 1? or x = -1? what role does it play in the loss function?
I also don't understand the discretized log-likelihood function setup. I only sort of get the idea that x0 is an image so each pixel is in {0, ..., 255} discrete. But why does the integral give the probability mass of x0?
Any help would be greatly appreciated!
The text was updated successfully, but these errors were encountered:
Hi, I am confused about why do we scale the value of the x0 sample from x1 to [-1, 1].
I understand why when x is between (-1, 1), the log-likelihood would become L_{t-1} when t = 0.
But what about when x = 1? or x = -1? what role does it play in the loss function?
I also don't understand the discretized log-likelihood function setup. I only sort of get the idea that x0 is an image so each pixel is in {0, ..., 255} discrete. But why does the integral give the probability mass of x0?
Any help would be greatly appreciated!
The text was updated successfully, but these errors were encountered: