《ResNeXt: Aggregated Residual Transformations for Deep Neural Networks》論文筆記
1. 概述 論文提出了ResNet網絡升級版——ResNeXt網絡，以往提高模型準確率的方法都是加深網絡或者加寬網絡。然而隨着超參數數量的增加，網絡設計的難度和計算開銷也會增加。所以本文提出了ResNeXt結構，該結構可以在不增加參數複雜度的前提下提高模型的準確率，同時也可減少超參數數量。將論文中提出的方法在ILSVRC 2016分類數據集與COCO檢測數據集上都比原始ResNet網絡要好。 本文
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相關文章
- 1. Aggregated Residual Transformations for Deep Neural Networks
- 2. ResNeXt - Aggregated Residual Transformations for Deep Neural Networks
- 3. CNN--ResNeXt--Aggregated Residual Transformations for Deep Neural Networks
- 4. 論文筆記——Aggregated Residual Transformations for Deep Neural Networks(ResNeXt)
- 5. 【論文閱讀】Aggregated Residual Transformations for Deep Neural Networks Saining（ResNext）
- 6. 論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 7. [Paper Reading Note]Aggregated Residual Transformations for Deep Neural Networks
- 8. 【ResNeXt】Aggregated Residual Transformations for Deep Neural Networks (2017) 全文翻譯
- 9. 《2017-Aggregated Residual Transformations for Deep Neural Networks》論文閱讀
- 10. [論文閱讀]ResNetXt|Aggregated Residual Transformations for Deep Neural Networks