  This is an opinion paper. We hope to deliver a key message that current
visual recognition systems are far from complete, i.e., recognizing everything
that human can recognize, yet it is very unlikely that the gap can be bridged
by continuously increasing human annotations. Based on the observation, we
advocate for a new type of pre-training task named learning-by-compression. The
computational models (e.g., a deep network) are optimized to represent the
visual data using compact features, and the features preserve the ability to
recover the original data. Semantic annotations, when available, play the role
of weak supervision. An important yet challenging issue is the evaluation of
image recovery, where we suggest some design principles and future research
directions. We hope our proposal can inspire the community to pursue the
compression-recovery tradeoff rather than the accuracy-complexity tradeoff.
