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I was wondering what the rational is for using 3x3 convolutions as opposed to 1x1 convolutions in the output layer. As far as i know, the original U-net paper uses 1x1 sigmoid/softmax neurons in the output for pixelwise classification, but in you implementation you use 3x3. Why is that?
The text was updated successfully, but these errors were encountered:
Hello
I was wondering what the rational is for using 3x3 convolutions as opposed to 1x1 convolutions in the output layer. As far as i know, the original U-net paper uses 1x1 sigmoid/softmax neurons in the output for pixelwise classification, but in you implementation you use 3x3. Why is that?
The text was updated successfully, but these errors were encountered: