publication . Preprint . 2017

Deep Learning in the Automotive Industry: Applications and Tools

Luckow, Andre; Cook, Matthew; Ashcraft, Nathan; Weill, Edwin; Djerekarov, Emil; Vorster, Bennie;
Open Access English
  • Published: 30 Apr 2017
Abstract
Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Learning
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74 references, page 1 of 5

[1] Yoshua Bengio, Ian J. Goodfellow, and Aaron Courville. Deep learning. Book in preparation for MIT Press, 2015.

[2] Trevor J. Hastie, Robert John Tibshirani, and Jerome H. Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics. Springer, New York, 2009.

[3] Andre Luckow, Ken Kennedy, Fabian Manhardt, Emil Djerekarov, Bennie Vorster, and Amy Apon. Automotive big data: Applications, workloads and infrastructures. In Proceedings of IEEE Conference on Big Data, Santa Clara, CA, USA, 2015. IEEE.

[4] B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, F. Mujica, A. Coates, and A. Y. Ng. An Empirical Evaluation of Deep Learning on Highway Driving. ArXiv e-prints, April 2015.

[5] Dean Pomerleau. Rapidly adapting artificial neural networks for autonomous navigation. In Richard Lippmann, John E. Moody, and David S. Touretzky, editors, NIPS, pages 429-435. Morgan Kaufmann, 1990.

[6] J. Schmidhuber. Deep learning in neural networks: An overview. Neural Networks, 61:85-117, 2015. Published online 2014; based on TR arXiv:1404.7828 [cs.NE]. [OpenAIRE]

[7] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097-1105. Curran Associates, Inc., 2012.

[8] Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6):82-97, 2012.

[9] Yoshua Bengio, Aaron C. Courville, and Pascal Vincent. Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, abs/1206.5538, 2012. [OpenAIRE]

[10] Geoffrey Hinton and Ruslan Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504 - 507, 2006.

[11] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li. Imagenet large scale visual recognition challenge. CoRR, abs/1409.0575, 2014. [OpenAIRE]

[12] K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. ArXiv e-prints, December 2015.

[13] David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484-489, 01 2016.

[14] Nicolas Vasilache, Jeff Johnson, Michaël Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun. Fast convolutional nets with fbfft: A GPU performance evaluation. CoRR, abs/1412.7580, 2014. [OpenAIRE]

[15] NVIDIA cuDNN. https://developer.nvidia.com/cuDNN, 2015.

74 references, page 1 of 5
Abstract
Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Learning
Download from
74 references, page 1 of 5

[1] Yoshua Bengio, Ian J. Goodfellow, and Aaron Courville. Deep learning. Book in preparation for MIT Press, 2015.

[2] Trevor J. Hastie, Robert John Tibshirani, and Jerome H. Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics. Springer, New York, 2009.

[3] Andre Luckow, Ken Kennedy, Fabian Manhardt, Emil Djerekarov, Bennie Vorster, and Amy Apon. Automotive big data: Applications, workloads and infrastructures. In Proceedings of IEEE Conference on Big Data, Santa Clara, CA, USA, 2015. IEEE.

[4] B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, F. Mujica, A. Coates, and A. Y. Ng. An Empirical Evaluation of Deep Learning on Highway Driving. ArXiv e-prints, April 2015.

[5] Dean Pomerleau. Rapidly adapting artificial neural networks for autonomous navigation. In Richard Lippmann, John E. Moody, and David S. Touretzky, editors, NIPS, pages 429-435. Morgan Kaufmann, 1990.

[6] J. Schmidhuber. Deep learning in neural networks: An overview. Neural Networks, 61:85-117, 2015. Published online 2014; based on TR arXiv:1404.7828 [cs.NE]. [OpenAIRE]

[7] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097-1105. Curran Associates, Inc., 2012.

[8] Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6):82-97, 2012.

[9] Yoshua Bengio, Aaron C. Courville, and Pascal Vincent. Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, abs/1206.5538, 2012. [OpenAIRE]

[10] Geoffrey Hinton and Ruslan Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504 - 507, 2006.

[11] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li. Imagenet large scale visual recognition challenge. CoRR, abs/1409.0575, 2014. [OpenAIRE]

[12] K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. ArXiv e-prints, December 2015.

[13] David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484-489, 01 2016.

[14] Nicolas Vasilache, Jeff Johnson, Michaël Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun. Fast convolutional nets with fbfft: A GPU performance evaluation. CoRR, abs/1412.7580, 2014. [OpenAIRE]

[15] NVIDIA cuDNN. https://developer.nvidia.com/cuDNN, 2015.

74 references, page 1 of 5
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