  How can unlabeled video augment visual learning? Existing methods perform
"slow" feature analysis, encouraging the representations of temporally close
frames to exhibit only small differences. While this standard approach captures
the fact that high-level visual signals change slowly over time, it fails to
capture *how* the visual content changes. We propose to generalize slow feature
analysis to "steady" feature analysis. The key idea is to impose a prior that
higher order derivatives in the learned feature space must be small. To this
end, we train a convolutional neural network with a regularizer on tuples of
sequential frames from unlabeled video. It encourages feature changes over time
to be smooth, i.e., similar to the most recent changes. Using five diverse
datasets, including unlabeled YouTube and KITTI videos, we demonstrate our
method's impact on object, scene, and action recognition tasks. We further show
that our features learned from unlabeled video can even surpass a standard
heavily supervised pretraining approach.
