论文标题
BHN：一个大脑般的异质网络
BHN: A Brain-like Heterogeneous Network
论文作者
论文摘要
人脑以一种无监督的方式工作，而多个大脑区域对于照亮智力至关重要。受到这一点的启发，我们提出了一个类似脑的异质网络（BHN），可以合作学习大量的分布式表示和一种全球注意力表示。通过优化分布式，自我监督和梯度分离的目标函数，我们的模型可以改善其表示形式，这些函数是由实验中的图片或视频框架构成的。
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.
