google-deepmind / google-deepmind/sonnet
(Maybe) in-consistency between VQ-VAE paper and its implementation.
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Description
FIrst of all, maybe it is my misunderstanding of the paper, so hope somebody could explain it for me, thanks! :
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in the paper, the loss is defined as
where `e` is the codebook defined at the beginning of the Section:
So, in the paper, the codebook loss and commitment loss are MSE **between `z_e(x)` and `e`**.
However, in the implementation, they are implemented as MSE **between `z_e(x)`(inputs) and `z_q(x)`(quantized)**, where variable quantized means quantized encoding of the image, namely `z_q`:
Are they actually the same thing? why?
- If the paper stated is right. how the dimension matches between `z_e(x)`(H' * W' * D) and `e`(K * D)?
- if the implementation is right. how `z_q(x)`(quantized) backprop since its calculation contains argmin?
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