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Hi @EscVM,
to me, it seems like the depth wise separable convolution implemented in https://github.com/EscVM/Efficient-CapsNet/blob/705449c/utils/layers.py#L123 lacks the point-wise convolution part?!
Shouldn't there be a second Conv2D layer with F filters of kernel size (1,1)?
Otherwise, you're doing a simple depth-wise convolution, right?
I am referencing https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728 here btw. Not sure if you gave a reference for the depthwise separable convolution in your paper.
The text was updated successfully, but these errors were encountered:
Hi @TobiasB22,
Yes, you are right! As specified in the pre-print paper version, no point-wise convolution operation is needed to construct the primary capsule layer.
Sorry, something went wrong.
Ok, thanks! I guess I must've skipped that part while reading the paper.
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Hi @EscVM,
to me, it seems like the depth wise separable convolution implemented in https://github.com/EscVM/Efficient-CapsNet/blob/705449c/utils/layers.py#L123 lacks the point-wise convolution part?!
Shouldn't there be a second Conv2D layer with F filters of kernel size (1,1)?
Otherwise, you're doing a simple depth-wise convolution, right?
I am referencing https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728 here btw.
Not sure if you gave a reference for the depthwise separable convolution in your paper.
The text was updated successfully, but these errors were encountered: