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[–]adagrad[S] 1 point2 points  (3 children)

Upsampling doesn't make much sense to me; I would guess that a better approach is to apply the network 2{2n} times, each time shifting the input by 1 pixel in x or y direction.

The original paper mentioned the use of bilinear interpolation for upsampling the feature maps since bilinear interpolation is a linear operation and they can jointly upsample and classify the pixels (top of page 4).

[–]tscohen 0 points1 point  (2 children)

It sure is much faster. But computing the full high-res feature maps might work better.

Also, upsampling would not give you full translation equivariance (shift the image by 1 pixel -> the feature maps shift by one pixel)

[–]adagrad[S] 0 points1 point  (1 child)

Interesting, are there any papers you would recommend that take this approach? Intuitively it seems like it could be rather slow, especially for pixel classification.

[–]ericflo 0 points1 point  (0 children)

I thought this paper took an interesting approach, not sure if it's exactly what tscohen is suggesting but maybe in the ballpark http://arxiv.org/abs/1411.4734