title: How does upsampling in Fully Connected Convolutional network work?
body: <p>I read several posts / articles and have some doubts on the mechanism of upsampling after the CNN downsampling.</p>
  
  <p>I took the 1st answer from this question:
  <a href="https://www.quora.com/How-do-fully-convolutional-networks-upsample-their-coarse-output" rel="noreferrer">https://www.quora.com/How-do-fully-convolutional-networks-upsample-their-coarse-output</a></p>
  
  <p>I understood that similar to normal convolution operation, the "upsampling" also uses kernels which need to be trained.</p>
  
  <p>Question1: if the "spatial information" is already lost during the first stages of CNN, how can it be re-constructed in anyway ?</p>
  
  <p>Question2: Why >"Upsampling from a small (coarse) featuremap deep in the network has good semantic information but bad resolution. Upsampling from a larger feature map closer to the input, will produce better detail but worse semantic information" ?</p>