In U-nets I would like a more straight-forward/detailed explanation in how the upsampling part ("right part of the U") is performed.
I read that it can be done by "transposed convolution layers" aka. "deconv layers". I would like a clear example (possibly a bit simplified) on how that is performed?
e.g. initialization, why/how? Detailed sizes/shapes input output etc?
e.g. from here https://medium.com/@keremturgutlu/semantic-segmentation-u-net-part-1-d8d6f6005066 : "In transposed convolutions we have weights that we learn through back-propagation. In papers I’ve come across all of these upsampling methods for various cases and also in practice you may change your architecture and try all of them to see which works best for your own problem. I personally prefer transposed convolutions since we have more control over it but you may go for bilinear interpolation or nearest neighbor for simplicity as well."
I don't understand the stated "ou may go for bilinear interp etc."
why would we choose a fixed interp method over learned transposed conv2D filter? How can that make sense?