BHN: A Brain-like Heterogeneous Network
Tao Liu

TL;DR
This paper introduces BHN, a brain-inspired heterogeneous network that learns distributed and global representations through self-supervised, gradient-isolated optimization, enhancing image and video understanding.
Contribution
It presents a novel brain-like architecture that cooperatively learns multiple representations using a minimax optimization strategy, inspired by brain functions.
Findings
Improved representation quality in image and video tasks
Effective self-supervised learning with gradient isolation
Demonstrated advantages over traditional models
Abstract
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.
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Neural dynamics and brain function · Domain Adaptation and Few-Shot Learning
