Optimizing Deep Learning Models for Resource‐Constrained Environments With Cluster‐Quantized Knowledge Distillation
ABSTRACT Deep convolutional neural networks (CNNs) are highly effective in computer vision tasks but remain challenging to deploy in resource‐constrained environments due to their high computational and memory requirements. Conventional model compression techniques, such as pruning and post‐training...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Wiley
2025-05-01
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| coleção: | Engineering Reports |
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| Acesso em linha: | https://doi.org/10.1002/eng2.70187 |
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