[論文解讀] DSD -- Dense-Sparse-Dense Training for Neural Network
《DSD: Dense-Sparse-Dense Training for Neural Network》發表在ICLR17， 這是一篇關注於提升模型訓練得到的準確率的文章，而不是一作傳統的研究領域：模型壓縮。 DSD是一種新的訓練模型的方式，可以提高預訓練模型的準確率。DSD和dropout不一樣，雖然都是在訓練過程中有prune（剪枝）操作，但是DSD是有一定依據來選擇去掉哪些connecti
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相關文章
- 1. Tips for Training Deep Neural Network
- 2. Training Recurrent Neural Network
- 3. 論文解讀-Bag of Freebies for Training Object Detection Neural Networks
- 4. 文獻閱讀（24）KDD2020-GCC：Graph Contrastive Coding for Graph Neural Network Pre-Training
- 5. 論文解讀：RECURRENT NEURAL NETWORK REGULARIZATION
- 6. 論文閱讀：Bag of Freebies for Training Object Detection Neural Networks
- 7. 粗讀CVPR2019論文 Edge-Labeling Graph Neural Network for Few-shot Learning
- 8. 【論文解讀 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 9. [論文解讀]A Quantitative Analysis Framework for Recurrent Neural Network
- 10. ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs論文解讀