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Reducing DNN labell...
Reducing DNN labelling cost using surprise adequacy: An industrial case study for autonomous driving
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- Kim, Jinhan (författare)
- Korea Advanced Institute of Science and Technology (KAIST)
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- Ju, Jeongil (författare)
- Hyundai Motor Group
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- Feldt, Robert, 1972 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Yoo, Shin (författare)
- Korea Advanced Institute of Science and Technology (KAIST)
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(creator_code:org_t)
- 2020-11-08
- 2020
- Engelska.
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Ingår i: ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering. - New York, NY, USA : ACM. ; , s. 1466-1476
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Deep Neural Network
- Software Testing
- Autonomous Driving
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)