  The human brain works in an unsupervised way, and more than one brain region
is essential for lighting up intelligence. Inspired by this, we propose a
brain-like heterogeneous network (BHN), which can cooperatively learn a lot of
distributed representations and one global attention representation. By
optimizing distributed, self-supervised, and gradient-isolated objective
functions in a minimax fashion, our model improves its representations, which
are generated from patches of pictures or frames of videos in experiments.
