《DropBlock: A regularization method for convolutional networks》笔记
Introduction Dropout的思想是随机失活一部分激活单元，让输出的feature或feature map丢失一些信息，使得网络能够关注更多的有辨别能力的特征，而不是只关注某几个特征，从而使得网络更加鲁棒，从另一方面来说，dropout起到了正则化的作用。 Dropout应用到卷积网络的feature map上，具体操作是在每个feature map上随机失活部分神经元。DropBlo
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相关文章
- 1. DropBlock： A regularization method for convolutional networks
- 2. （原）DropBlock A regularization method for convolutional networks
- 3. DropBlock: A regularization method for convolutional networks论文解读
- 4. HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs--论文笔记
- 5. Regularization method for machine learning
- 6. 【论文笔记】Fully Convolutional Networks for Semantic Segmentation
- 7. Deformable Convolutional Networks笔记
- 8. Learning Convolutional Neural Networks for Graphs论文笔记
- 9. 学习笔记：Fully Convolutional Networks for Segmantic Segmentation
- 10. Improved Regularization of Convolutional Neural Networks with Cutout论文笔记