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

BHN: A Brain-like Heterogeneous Network