  We show that many perception tasks, from visual recognition, semantic
segmentation, optical flow, depth estimation to vocalization discrimination,
are highly redundant functions of their input data. Images or spectrograms,
projected into different subspaces, formed by orthogonal bases in pixel,
Fourier or wavelet domains, can be used to solve these tasks remarkably well
regardless of whether it is the top subspace where data varies the most, some
intermediate subspace with moderate variability--or the bottom subspace where
data varies the least. This phenomenon occurs because different subspaces have
a large degree of redundant information relevant to the task.
