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September 10, 2025Applied and Computational EngineeringOpen Access

Lightweight Network Architecture Optimization of Deep Learning Models

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ZMZijing MaoBeijing Haidian Hospital

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Implication

Proposed LwNet enhances feature extraction and efficiency in lightweight convolutional networks, suggesting improved accuracy in resource-limited environments.

Key Points

LwNet achieves a high classification accuracy on the CIFAR-10 dataset, balancing performance and efficiency.
The model incorporates innovative techniques like LightBlock, combining various convolution methods and attention mechanisms.
Experimental results indicate that LwNet outperforms other lightweight networks like MobileNet and SqueezeNet.
The study provides reproducible code and data, supporting further research in lightweight neural network architectures.

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Zijing Mao (2025) studied this question.

synapsesocial.com/papers/68c183f09b7b07f3a060f896 https://doi.org/10.54254/2755-2721/2025.ast26509

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Implication

Proposed LwNet enhances feature extraction and efficiency in lightweight convolutional networks, suggesting improved accuracy in resource-limited environments.

Lightweight Network Architecture Optimization of Deep Learning Models

Discussion

Member takes

Implication

Key Points

Cite This Study

Lightweight Network Architecture Optimization of Deep Learning Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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