论文标题
功能匹配在驾驶场景识别中的有效性
Effectiveness of Function Matching in Driving Scene Recognition
论文作者
论文摘要
知识蒸馏是一种有效的方法，用于训练自动驾驶中所需的紧凑型识别者。关于图像分类的最新研究表明，在广泛的数据点上匹配的学生和老师对于提高蒸馏的性能至关重要。这个概念（称为功能匹配）适用于驾驶场景识别，通常可以提供几乎无限的未标记数据。在这项研究中，我们通过实验研究了使用如此大量的未标记数据进行蒸馏的影响，以便在自主驾驶的结构化预测任务中的学生模型的性能。通过大量的实验，我们证明了紧凑的学生模型的表现可以大大提高，甚至可以通过知识蒸馏和大量未标记的数据来匹配大规模教师的表现。
Knowledge distillation is an effective approach for training compact recognizers required in autonomous driving. Recent studies on image classification have shown that matching student and teacher on a wide range of data points is critical for improving performance in distillation. This concept (called function matching) is suitable for driving scene recognition, where generally an almost infinite amount of unlabeled data are available. In this study, we experimentally investigate the impact of using such a large amount of unlabeled data for distillation on the performance of student models in structured prediction tasks for autonomous driving. Through extensive experiments, we demonstrate that the performance of the compact student model can be improved dramatically and even match the performance of the large-scale teacher by knowledge distillation with massive unlabeled data.
