Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks論文初讀
目錄 摘要 引言 相關工作 KPN 總覽 KPN層設計 多路多階段訓練 迭代刪減選擇映射路徑 實驗結果 數據集上的分析 自動路徑選擇方法的分析 結論 摘要 作者提出了一種teacher-student映射知識蒸餾的方法，在小數據集上有效 teacher和student用來對接的中間層可以通過自適應的方式來選擇，這種自適應的方式是以一種迭代的方式通過評估聯合損失來完成的 引言
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相關文章
- 1. 論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
- 2. [論文筆記]Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
- 3. 論文閱讀：Deep Neural Networks for Object Detection
- 4. [論文解讀] Concolic Testing for Deep Neural Networks
- 5. Neural Networks and Deep Learning-引論
- 6. 論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 7. 【論文閱讀筆記】---《A Survey of Model Compression and Acceleration for Deep Neural Networks》
- 8. Neural Networks and Deep Learning
- 9. 《Deep Back-Projection Networks For Super-Resolution》論文閱讀之DBPN
- 10. COMPRESSION OF DEEP CONVOLUTIONAL NEURAL NETWORKS FOR FAST AND LOW POWER MOBILE APPLICATIONS論文解讀