  We propose to modify the common training protocols of optical flow, leading
to sizable accuracy improvements without adding to the computational complexity
of the training process. The improvement is based on observing the bias in
sampling challenging data that exists in the current training protocol, and
improving the sampling process. In addition, we find that both regularization
and augmentation should decrease during the training protocol.
  Using an existing low parameters architecture, the method is ranked first on
the MPI Sintel benchmark among all other methods, improving the best two frames
method accuracy by more than 10%. The method also surpasses all similar
architecture variants by more than 12% and 19.7% on the KITTI benchmarks,
achieving the lowest Average End-Point Error on KITTI2012 among two-frame
methods, without using extra datasets.
