論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
文章目錄 1、論文總述 2、vgg/resnet 與 Inception系列網絡設計機制的不同點 3、ResNeXt並不是ensembling 4、兩個設計準則 5、split-transform-merge的本質 6、BN和Relu的位置 1、論文總述 這篇論文提出的網絡是resnet的升級版，設計思想結合了vgg/resnet 的stacking building blocks 以及 Ince
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相關文章
- 1. 【論文閱讀】Aggregated Residual Transformations for Deep Neural Networks Saining（ResNext）
- 2. ResNeXt - Aggregated Residual Transformations for Deep Neural Networks
- 3. CNN--ResNeXt--Aggregated Residual Transformations for Deep Neural Networks
- 4. Aggregated Residual Transformations for Deep Neural Networks
- 5. 論文筆記——Aggregated Residual Transformations for Deep Neural Networks(ResNeXt)
- 6. 《ResNeXt: Aggregated Residual Transformations for Deep Neural Networks》論文筆記
- 7. 《2017-Aggregated Residual Transformations for Deep Neural Networks》論文閱讀
- 8. [論文閱讀]ResNetXt|Aggregated Residual Transformations for Deep Neural Networks
- 9. 【ResNeXt】Aggregated Residual Transformations for Deep Neural Networks (2017) 全文翻譯
- 10. [Paper Reading Note]Aggregated Residual Transformations for Deep Neural Networks