dpp
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positive bias.py
Hi, I'm confused about this code in positive bias.py,
First, why did you do x/xn before geting w, not the same as the one introduced in the paper.
Second, ID is the result of a box filter applied to the input followed by downsampling, which is subsequently smoothed by an approximate 2D Gaussian filter, but you only use a pooling replace it.
dividing x by xn is just for numerical stability and does not change the value of the output result as it cancels out. for the DPP complete implementation look at DPP_asym_lite.lua in visinf _package.