Lightweight models for weather identification

Wang, Congcong; Liu, Pengyu; Jia, Kebin; Chen, Siwei

doi:10.1109/apsipaasc47483.2019.9023242

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Moreover, lightweight DNNs can also be used in other sensing and surveillance scenarios. In particular, Wang et al [191] combined the structure of the inverted residuals in MobileNetV2 and the SE module in SENet to design a lightweight neural network for weather monitoring. This model can effectively reduce the memory cost.…”

Section: ) Intelligent Transportation Systems (Its)mentioning

confidence: 99%

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

Zhang

Ying

Dai

et al. 2021

IEEE Internet Things J.

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Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

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Section: ) Intelligent Transportation Systems (Its)mentioning

confidence: 99%