論文閱讀：Reducing Transformer Depth On Demand With Structured Dropout
Introduction 這篇paper是做Transformer壓縮的，但其實bert的核心也就是transformer，這篇paper的實驗裏也做了bert的壓縮。作者的主要工作是提出了LayerDrop的方法，即一種結構化的dropout的方法來對transformer模型進行訓練，從而在不需要fine-tune的情況下選擇一個大網絡的子網絡。 這篇paper方法的核心是通過Dropout來
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相關文章
- 1. 論文閱讀：Spatial Transformer Networks
- 2. 論文閱讀：Spatial Transformer Network
- 3. 論文--閱讀翻譯筆記-Reducing the Dimensionality of Data with Neural Networks
- 4. 【論文閱讀筆記】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
- 5. Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention閱讀筆記
- 6. 【論文閱讀】AlexNet:ImageNet Classification with Deep Covolutional Network
- 7. 論文閱讀：Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
- 8. Anytime Stereo Image Depth Estimation on Mobile Devices論文閱讀筆記
- 9. 【論文閱讀】Structured Knowledge Distillation for Semantic Segmentation
- 10. (論文閱讀)Deeper Depth Prediction with Fully Convolutional Residual Networks