for two additional iterations to increase accuracy. The batch size is set to 100 and $m$ to 1.

To evaluate the quality of our optical flow results we calculate the endpoint error (EPE) for non-occluded areas (noc) as well as occluded + non-occluded areas (all). (noc) is a more direct measure as CNNs are only trained here. However, the interpolation into occluded areas (like *Flow Fields* we use EpicFlow [@revaud2015epicflow] for that) also depends on good matches close to the occlusion boundary, where matching is especially difficult due to partial occlusions of patches. Furthermore, like [@geiger2013vision], we measure the percentage of pixels with an EPE above a threshold in pixels (px).

Comparison of CNN based Multi-Scale Feature Map Approaches {#decisions}
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In Table \[ffmethods\], we compare the *original* feature creation approach (Figure \[ffscales\] left) with *our* approach (Figure \[ffscales\] right), with respect to our CNN features. We also examine two variants of our approach in the table: *nolowpass* which does not contain the “Low-Pass 2x” blocks and *all resolutions* which uses 1x,2x,4x,8x up/downsampling for the four scales (instead of 1x,1x,2x,4x in Figure \[ffscales\] right). The reason why *all resolutions* does not work well is demonstrated in Figure \[scales\] (a). Starting 