論文筆記: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
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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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