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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 - 01:30
01:00
2m
Talk
A Comprehensive Study on Challenges in Deploying Deep Learning Based Software
Research Papers
Zhenpeng Chen Peking University, China, Yanbin Cao Peking University, China, Yuanqiang Liu Peking University, China, Haoyu Wang Beijing University of Posts and Telecommunications, Tao Xie Peking University, Xuanzhe Liu Peking University, China
DOI Pre-print
01:03
1m
Talk
A First Look at the Integration of Machine Learning Models in Complex Autonomous Driving Systems: A Case Study on Apollo
Industry Papers
pengzi Concordia University, Canada, Jinqiu Yang Concordia University, Montreal, Canada, Tse-Hsun (Peter) Chen Concordia University, Lei Ma Kyushu University
DOI
01:05
1m
Talk
Enhancing the Interoperability between Deep Learning Frameworks by Model Conversion
Industry Papers
Yu David Liu SUNY Binghamton, USA, Cheng Chen ByteDance, China, Ru Zhang Microsoft Research, Tingting Qin Microsoft Research, China, Xiang Ji Microsoft Research, China, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research
DOI
01:07
1m
Talk
Estimating GPU Memory Consumption of Deep Learning Models
Industry Papers
Yanjie Gao Microsoft Research, China, Yu David Liu SUNY Binghamton, USA, Hongyu Zhang University of Newcastle, Australia, lizhengxian Microsoft Research, China, Yonghao Zhu Microsoft Research, China, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research
DOI
01:09
1m
Talk
IntelliCode Compose: Code Generation using Transformer
Industry Papers
Alexey Svyatkovskiy Microsoft, Shao Kun Deng Microsoft Corporation, Shengyu Fu Microsoft, USA, Neel Sundaresan Microsoft Corporation
DOI Pre-print
01:11
1m
Talk
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving
Industry Papers
Jinhan Kim KAIST, Jeongil Ju Hyundai Motor Group, South Korea, Robert Feldt Chalmers University of Technology, Sweden, Shin Yoo Korea Advanced Institute of Science and Technology
DOI Pre-print
01:13
17m
Conversations on ML In Practice
Research Papers
Sidong Feng Australian National University, Australia, Tse-Hsun (Peter) Chen Concordia University, Yanbin Cao Peking University, China, Yanjie Gao Microsoft Research, China, Zhenpeng Chen Peking University, China, M: Joshua Garcia University of California, Irvine