Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More

CVPR 2019 Jingwen YeYixin JiXinchao WangKairi OuDapeng TaoMingli Song

In this paper, we investigate a novel deep-model reusing task. Our goal is to train a lightweight and versatile student model, without human-labelled annotations, that amalgamates the knowledge and masters the expertise of two pretrained teacher models working on heterogeneous problems, one on scene parsing and the other on depth estimation... (read more)

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