Filter Forests for Learning DataDependent Convolutional Kernels Sean Ryan Fanello Cem Keskin Pushmeet Kohli Shahram Izadi Jamie Shotton Antonio Criminisi Ugo Pattacini Tim Paek Microsoft Research iC
FF can be used for general signal restoration tasks that can be tackled via convolutional 64257lter ing where it attempts to learn the optimal 64257ltering kernels to be applied to each data point The model can learn both the size of the kernel and
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