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Presentation
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Abstract:
As deep learning algorithms for autonomous driving have progressed from early semantic segmentation to today's advanced systems, their consumption of data and computational resources has increased. The amount of data acquired and the need for annotations are growing exponentially, creating new challenges for improving accuracy and achieving desired safety levels. This session will explore some of these hurdles and discuss our proposed solutions in terms of active learning, computational efficiency, and the efficient use of synthetic data for training deep neural networks.
As deep learning algorithms for autonomous driving have progressed from early semantic segmentation to today's advanced systems, their consumption of data and computational resources has increased. The amount of data acquired and the need for annotations are growing exponentially, creating new challenges for improving accuracy and achieving desired safety levels. This session will explore some of these hurdles and discuss our proposed solutions in terms of active learning, computational efficiency, and the efficient use of synthetic data for training deep neural networks.  Back
 
Topics:
Autonomous Vehicles, AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9630
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