How do I create a layer with separate weights? I’m trying to have code that can “interpolate” points in a 2d plane, but interpolated points of the outputs just “stack” on the same input points, and I thought separating the weights would help reduce the stacking that I observe.

Can you elaborate on what did you mean by this?

It will be great if you can post some code examples on what have you tried.

I’m currently attempting to reimplement at first, a 2d version of Pu-net, but I’ve given up on that and went straight to a “cut-down” Pointnet Version. From the PU-net paper:

Hence, we

further add another convolution (with separate weights) for

each feature set. Since we train the network to learn the r

different convolutions for the r feature sets, these new features can include more diverse information, thus reducing

their correlations.

In the implementation, they seemed to be not adding a relu activation functions, so I tried that, but that didn’t seem to have the desired effect.

In my understanding (from Fig.1 of the PU-Net paper), they are applying `r`

different convolutional pipelines (each pipeline has 2 conv layers with its own weights) to get r different (non-linear) affine projections of point-features. i.e., `N x C`

goes to `r * (N x C2)`

. I do not understand their reason for adding C_2 layer. However, its adding more capacity (parameters) to the model.

Implementation could be:

```
def __init__(...):
self.expansion_set = nn.ModuleList()
for i in range(r):
self.expansion_set.append(nn.Sequential(nn.Conv1d(C, C1, kernel_size=1),
nn.ReLU(),
nn.Conv1d(C1, C2, kernel_size=1)))
...
def forward(...):
...
r_sets = []
for i in range(r):
r_set.append(self.expansion_set[i](features))
```