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Sorry to bother you. I have worked with your code for a couple of days now. I am very excited and I found a couple of things that might be relevant.
Here is another one. In the paper you mention Multi-Layer Perceptrons as the basis for the transformer/attention-layers. So far so good. In the implementation, there are a lot of 2D convolutions with kernel-size (1, 1). I think this turns the convolutions effectively into plain dense-layers.
Example: https://github.com/FrankCAN/GAPointNet/blob/master/models/network.py#L64
What do you think?
The text was updated successfully, but these errors were encountered:
Hello!
Sorry to bother you. I have worked with your code for a couple of days now. I am very excited and I found a couple of things that might be relevant.
Here is another one. In the paper you mention Multi-Layer Perceptrons as the basis for the transformer/attention-layers. So far so good. In the implementation, there are a lot of 2D convolutions with kernel-size (1, 1). I think this turns the convolutions effectively into plain dense-layers.
Example:
https://github.com/FrankCAN/GAPointNet/blob/master/models/network.py#L64
What do you think?
The text was updated successfully, but these errors were encountered: