《2017-Aggregated Residual Transformations for Deep Neural Networks》論文閱讀
如何評價谷歌的xception網絡？ 動機 傳統的要提高模型的準確率，都是加深或加寬網絡，但是隨着超參數數量的增加（比如channels數，filter size等等），網絡設計的難度和計算開銷也會增加。 本文提出的 ResNeXt 結構可以在不增加參數複雜度的前提下提高準確率，同時還減少了超參數的數量； 貢獻 作者在這篇論文中提出網絡 ResNeXt，同時採用 VGG 堆疊的思想和 Incept
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相關文章
- 1. 【論文閱讀】Aggregated Residual Transformations for Deep Neural Networks Saining（ResNext）
- 2. 論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 3. [論文閱讀]ResNetXt|Aggregated Residual Transformations for Deep Neural Networks
- 4. Aggregated Residual Transformations for Deep Neural Networks
- 5. ResNeXt - Aggregated Residual Transformations for Deep Neural Networks
- 6. CNN--ResNeXt--Aggregated Residual Transformations for Deep Neural Networks
- 7. [Paper Reading Note]Aggregated Residual Transformations for Deep Neural Networks
- 8. 論文筆記——Aggregated Residual Transformations for Deep Neural Networks(ResNeXt)
- 9. 《ResNeXt: Aggregated Residual Transformations for Deep Neural Networks》論文筆記
- 10. 論文閱讀：Deep Neural Networks for Object Detection