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This work is very interesting and useful.
I have some question about the code.
why do you use the torch.abs(x) in the end of the VGG forward.
In my point, we can use the result of self.reg_layer as the result of model forward.
So, I want to know the reason you use the torch.abs
Thank you very much
The text was updated successfully, but these errors were encountered:
Personally, I think the result should be non-negative, so they use torch.abs, although c = sum(y(x)p(x)) where y(x) could be negative. If the output could be negative, it might make the model convergence slow(I guess). Nobody want to see a negative value on a density map, right. Actually, I tried use Relu to replace the abs. Using abs is better than relu. It is interesting for me.
This work is very interesting and useful.
I have some question about the code.
why do you use the torch.abs(x) in the end of the VGG forward.
In my point, we can use the result of self.reg_layer as the result of model forward.
So, I want to know the reason you use the torch.abs
Thank you very much
The text was updated successfully, but these errors were encountered: