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Aug, 2020
通过任务蒸馏进行领域适应
Domain Adaptation Through Task Distillation
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Brady Zhou, Nimit Kalra, Philipp Krähenbühl
TL;DR
使用图像识别数据集作为源域和目标域之间的桥梁,通过任务蒸馏框架,在不同仿真器之间成功地传输导航策略,并在传统领域适应基准上展现出有前途的结果。
Abstract
deep networks
devour millions of precisely annotated images to build their complex and powerful representations. Unfortunately, tasks like
autonomous driving
have virtually no real-world training data. Repeatedly
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