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I have read some materials and papers. None of them have mentioned if there are bias terms like B_phi, B_g and B_theta.
I have tried my implementation with Bias=True, just like you did, and it did improve the performance.
I just want to ask where did you come to this idea of setting bias=True(as default). Just in case if I was missing something in my reading.
The text was updated successfully, but these errors were encountered:
Hi @liucu0135 . In fact, I omitted this point.
I guess that bias parameter can improve fitting ability of models. And in some algorithms, in order to avoid overfitting, the Conv layers often don't contain bias parameters.
Hello, I noticed you did not set bias=False in the 1x1 3d convolution layers which implements
phi=W_phix+B_phi
g=W_gx+B_g
theta=W_theta*x+B_theta
I have read some materials and papers. None of them have mentioned if there are bias terms like B_phi, B_g and B_theta.
I have tried my implementation with Bias=True, just like you did, and it did improve the performance.
I just want to ask where did you come to this idea of setting bias=True(as default). Just in case if I was missing something in my reading.
The text was updated successfully, but these errors were encountered: