Model Compression Using Optimal Transport

Model compression methods are important to allow for easier deployment of deep learning models in compute, memory and energy-constrained environments such as mobile phones. Knowledge distillation is a class of model compression algorithms where knowledge from a large teacher network is transferred t...

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Veröffentlicht in:	Proceedings / IEEE Workshop on Applications of Computer Vision S. 3645 - 3654
Hauptverfasser:	Lohit, Suhas, Jones, Michael
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2022
Schlagworte:	Computational modeling Computer vision Deep learning Deep Learning -> Efficient Training and Inference Methods for Networks Deep Learning; Object Detection/Recognition/Categorization; Statistical Methods; Learning and Optimization Image coding Knowledge engineering Mobile handsets Training
ISSN:	2642-9381
Online-Zugang:	Volltext
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