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論文筆記: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
時間 2020-12-30
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論文下載 arxiv paper code Abstract 使用"Feature Extraction+Region Proposal+RoI Classification" 的結構,主要對Feature Extraction進行重新設計。因爲,Region Proposal部分計算量不太大而且classification部分可以使用通用的技術(例如:Truncated SVD) 進行有效的壓縮
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論文閱讀--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
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相關文章
1.
論文閱讀--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
2.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - arxiv 2016.08
3.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
4.
【PVANet】《PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection》
5.
Deep Neural Networks for Object Detection論文翻譯
6.
論文閱讀:Deep Neural Networks for Object Detection
7.
深度學習【51】物體檢測:PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
8.
Deep Neural Networks for Object Detection
9.
【論文閱讀筆記】Deep Neural Networks for Object Detection
10.
【目標檢測】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
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