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Recent applications pose requirements of both cross-domain knowledge transfer and model compression to machine learning models due to insufficient training data and limited computational resources. In this paper, we propose a new knowledge distillati
Externí odkaz:
http://arxiv.org/abs/2104.14696
Semantic segmentation of road scenes is one of the key technologies for realizing autonomous driving scene perception, and the effectiveness of deep Convolutional Neural Networks(CNNs) for this task has been demonstrated. State-of-art CNNs for semant
Externí odkaz:
http://arxiv.org/abs/2103.13733
Model compression becomes a recent trend due to the requirement of deploying neural networks on embedded and mobile devices. Hence, both accuracy and efficiency are of critical importance. To explore a balance between them, a knowledge distillation s
Externí odkaz:
http://arxiv.org/abs/2010.13500
Autor:
Yang, ZongMin, Vinodh, Rajangam, Balakrishnan, Balamuralidharan, Rajmohan, Rajendiran, Kim, Hee-Je
Publikováno v:
In Journal of Energy Storage April 2020 28
Autor:
Vinodh, Rajangam, Muralee Gopi, Chandu V.V., Yang, ZongMin, Deviprasath, Chinnadurai, Atchudan, Raji, Raman, Vivekanandan, Yi, Moonsuk, Kim, Hee-Je
Publikováno v:
In Journal of Energy Storage April 2020 28
Akademický článek
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Autor:
Yang, ZongMin, Vinodh, Rajangam, Balakrishnan, Balamuralidharan, Rajmohan, Rajendiran, Kim, Hee-Je
Publikováno v:
Jouranl of Energy Storage; April 2020, Vol. 28 Issue: 1