Optical Sensor Based Gestures Inference Using Recurrent Neural Network in Mobile Conditions

Czuszynski, Krzysztof; Kwaśniewska, Alicja; Szankin, Maciej; Rumiński, Jacek

doi:10.1109/hsi.2018.8430823

Cited by 4 publications

(1 citation statement)

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“…The authors identified the challenge of dealing with resource constraints on these mobile devices and the use of knowledge distillation techniques to address some of these challenges. Czuszynski et al (2018) presented hand-gesture recognition using recurrent neural networks (RNNs) deployed on smartphones. The idea of HAR based on spatio-temporal features from IoT devices like a cup, a toothbrush and a fork was presented in Lopez Medina et al (2019) .…”

Section: Applications Of Knowledge Distillationmentioning

confidence: 99%

Knowledge distillation in deep learning and its applications

Alkhulaifi¹,

Alsahli²,

Ahmad³

2021

PeerJ Computer Science

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Deep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices. One possible solution is knowledge distillation whereby a smaller model (student model) is trained by utilizing the information from a larger model (teacher model). In this paper, we present an outlook of knowledge distillation techniques applied to deep learning models. To compare the performances of different techniques, we propose a new metric called distillation metric which compares different knowledge distillation solutions based on models' sizes and accuracy scores. Based on the survey, some interesting conclusions are drawn and presented in this paper including the current challenges and possible research directions.

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Section: Applications Of Knowledge Distillationmentioning

confidence: 99%