Does Conv2d filter need bias per weights?

vision
1

Let’s say I have an image with (3,32,32) size and I want to apply (32,4,4) filter.
If I use “torch.nn.Conv2d” then
image
by this formula, I have 32 bias values.
If let’s say adding bias at each weight, then I’ll have 32x(4x4)= 512 bias values.
Is there any reason not applying bias terms at each weight in the Conv2d filter?

2

The filter will be applied as an elementwise multiplication to the current input window and summed afterwards to create a scalar output value. The bias will then be added to the scalar value, which explains the shape.