@@ -59,3 +59,16 @@ The evaluation of the geometric classes is fine.
*[FCN-32s PASCAL-Context](pascalcontext-fcn32s): single stream, 32 pixel prediction stride net
*[FCN-16s PASCAL-Context](pascalcontext-fcn16s): two stream, 16 pixel prediction stride net
*[FCN-8s PASCAL-Context](pascalcontext-fcn8s): three stream, 8 pixel prediction stride net
## Freqeuntly Asked Questions
**Is learning the interpolation necessary?** In our original experiments the interpolation layers were initialized to bilinear kernels and then learned.
In follow-up experiments, and this reference implementation, the bilinear kernels are fixed.
There is no significant difference in accuracy in our experiments, and fixing these parameters gives a slight speed-up.
Note that in our networks there is only one interpolation kernel per output class, and results may differ for higher-dimensional and non-linear interpolation, for which learning may help further.
**Why pad the input?**: The 100 pixel input padding guarantees that the network output can be aligned to the input for any input size in the given datasets, for instance PASCAL VOC.
The alignment is handled automatically by net specification and the crop layer.
It is possible, though less convenient, to calculate the exact offsets necessary and do away with this padding.
**What about FCN-GoogLeNet?**: a reference FCN-GoogLeNet for PASCAL VOC is coming soon.
