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Tue 10 Nov 2020 01:11 - 01:12 at Virtual room 1 - ML In Practice

Deep Neural Networks (DNNs) are rapidly being adopted by the automotive industry, due to their impressive performance in tasks that are essential for autonomous driving. Object segmentation is one such task: its aim is to precisely locate boundaries of objects and classify the identified objects, helping autonomous cars to recognise the road environment and the traffic situation. Not only is this task safety critical, but developing a DNN based object segmentation module presents a set of challenges that are significantly different from traditional development of safety critical software. The development process in use consists of multiple iterations of data collection, labelling, training, and evaluation. Among these stages, training and evaluation are computation intensive while data collection and labelling are manual labour intensive. This paper shows how development of DNN based object segmentation can be improved by exploiting the correlation between Surprise Adequacy (SA) and model performance. The correlation allows us to predict model performance for inputs without manually labelling them. This, in turn, enables understanding of model performance, more guided data collection, and informed decisions about further training. In our industrial case study the technique allows cost savings of up to 50% with negligible evaluation inaccuracy. Furthermore, engineers can trade off cost savings versus the tolerable level of inaccuracy depending on different development phases and scenarios.

Tue 10 Nov
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01:00 - 01:02
Talk
Research Papers
Zhenpeng ChenPeking University, China, Yanbin CaoPeking University, China, Yuanqiang LiuPeking University, China, Haoyu WangBeijing University of Posts and Telecommunications, Tao XiePeking University, Xuanzhe LiuPeking University, China
DOI Pre-print
01:03 - 01:04
Talk
Industry Papers
pengzi Concordia University, Canada, Jinqiu YangConcordia University, Montreal, Canada, Tse-Hsun (Peter) ChenConcordia University, Lei MaKyushu University
DOI
01:05 - 01:06
Talk
Industry Papers
Yu David LiuSUNY Binghamton, USA, Cheng ChenByteDance, China, Ru ZhangMicrosoft Research, Tingting QinMicrosoft Research, China, Xiang JiMicrosoft Research, China, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research
DOI
01:07 - 01:08
Talk
Industry Papers
Yanjie GaoMicrosoft Research, China, Yu David LiuSUNY Binghamton, USA, Hongyu ZhangUniversity of Newcastle, Australia, lizhengxian Microsoft Research, China, Yonghao ZhuMicrosoft Research, China, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research
DOI
01:09 - 01:10
Talk
Industry Papers
Alexey SvyatkovskiyMicrosoft, Shao Kun DengMicrosoft Corporation, Shengyu FuMicrosoft, USA, Neel SundaresanMicrosoft Corporation
DOI Pre-print
01:11 - 01:12
Talk
Industry Papers
Jinhan KimKAIST, Jeongil JuHyundai Motor Group, South Korea, Robert FeldtChalmers University of Technology, Sweden, Shin YooKorea Advanced Institute of Science and Technology
DOI Pre-print
01:13 - 01:30
Research Papers
Sidong FengAustralian National University, Australia, Tse-Hsun (Peter) ChenConcordia University, Yanbin CaoPeking University, China, Yanjie GaoMicrosoft Research, China, Zhenpeng ChenPeking University, China, M: Joshua GarciaUniversity of California, Irvine