Combining 2 Neural Networks
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31-10-2019 - |
Question
2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ...). Defining my forward function as follows:
def forward(self,x1,x2):
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
Now for the similarity measure:
out_cat = torch.cat([out_conv3a, out_conv3b],dim=1)
futher_conv = nn.Conv2d(out_cat)
Question is as follows:
Would Depthwise/Separable Convolutions as in the google paper yield any advantage over 2d convolution of the concatenated input. For that matter can convolution be a similarity measure, cross correlation and convolution are very similar.
It is my understanding that the
groups=2
option inconv2d
would provide 2 separate inputs to train weights with, in this case each of the previous networks weights. How are these combined afterwards?
No correct solution