論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
本文提出一種新的設定，開始和以前一樣，使用一個預訓練的大網絡來指導一個更窄更快的小網絡訓練，但是用來訓練的數據是新的並且標籤信息有限，所以這個場景包括了不同數據域的自適應和模型壓縮 加入了一個知識投影矩陣，是學習得到的，選擇大模型的某處中間層，經過投影，加入到小模型的某處，這個過程也是學習的 整個模型的系統如下 KNOWLEDGE PROJECTION NETWORK 知識投影網絡 1、overv
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相關文章
- 1. Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks論文初讀
- 2. 論文閱讀：Deep Neural Networks for Object Detection
- 3. 論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 4. 《Deep Back-Projection Networks For Super-Resolution》論文閱讀之DBPN
- 5. [論文筆記]Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
- 6. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 7. 論文閱讀：Bag of Freebies for Training Object Detection Neural Networks
- 8. 【論文閱讀筆記】---《A Survey of Model Compression and Acceleration for Deep Neural Networks》
- 9. [論文解讀] Concolic Testing for Deep Neural Networks
- 10. 論文閱讀--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection