How to apply pair-wise distillation on depth prediction task

[mzy97]

Thank you for sharing this great work!
Q1: I wonder where to use pair-wise distillation loss, apply it at the end of the encoder (for example, 1/16*HW feature map of ResNet) or apply it at every scale of the encoder ( 1/16, 1/8, 1/4...)?
Q2: Can pair-wise distillation work when Teacher's encoder and Student's encoder has different downsample rate, (eg. student downsample input 1/8, while teacher downsamples input 1/16), or decoder structure?
Q3: Can this method used to distill from VNL to structure like FastDepth (different with VNL-student in the decoder), because VNL-student may have heavy decoder.

[djmth]

I‘m also confused about the distillation loss for the other two tasks, but especially about the pixel-wise loss.
The pixel-wise loss in the paper is for the segmentation task and is KL divergence, which is obviously not suitable for the depth task.
I really wonder how the pixel-wise loss is implemented, though the author explains this doesn't work for the depth task.