I'm trying to understand the deconv referenced in the paper Visualizing and Understanding Convolutional Networks
The paper states (section 2, p. 3):
the deconvnet uses transposed versions of the same filters, but applied to the rectified maps
Is it possible to implement this step in a short code example? Given an unpooled, rectified map; how would the transposed filter be applied against it?
I did try looking at the referenced paper Adaptive Deconvolutional Networks for Mid and High Level Feature Learning. However, I'm not wrapping my head around its explanations too well; and it references a third paper with regard to its work on "layers of convolutional sparse coding" (deconvolution [M. Zeiler, D. Krishnan, G. Taylor, and R. Fergus. Deconvolutional networks. In CVPR, 2010]), but this 2010 paper appears to require access to download.