《2017-Aggregated Residual Transformations for Deep Neural Networks》论文阅读
如何评价谷歌的xception网络？ 动机 传统的要提高模型的准确率，都是加深或加宽网络，但是随着超参数数量的增加（比如channels数，filter size等等），网络设计的难度和计算开销也会增加。 本文提出的 ResNeXt 结构可以在不增加参数复杂度的前提下提高准确率，同时还减少了超参数的数量； 贡献 作者在这篇论文中提出网络 ResNeXt，同时采用 VGG 堆叠的思想和 Incept
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相关文章
- 1. 【论文阅读】Aggregated Residual Transformations for Deep Neural Networks Saining（ResNext）
- 2. 论文阅读：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 3. [论文阅读]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. 论文笔记——Aggregated Residual Transformations for Deep Neural Networks(ResNeXt)
- 9. 《ResNeXt: Aggregated Residual Transformations for Deep Neural Networks》论文笔记
- 10. 论文阅读：Deep Neural Networks for Object Detection