What is exactly fully convolutional layer?

Question

What is exactly fully convolutaionl layer? I mean, why is it 'fully'? The wording in [Long] is quite confusing to me.

Is it because they never use fully connected layer? Or is it because the convolution layers obtained by the 'convolutionization' described in Figure 2 have their kernels cover their entire input regions?

Answer 1

Do you see the last part in this image " fully connected" in fully convolution network we remove this part. But then how can do classification since we already have many channels with big activation map ?

In the example you mentioned they do up-sampling and their cost function is to measure the error between the re-construed image (up-sampled) and the ground truth.

So why it is called fully convolution because it is just convolution there. spatial feature extraction.