[Paper note] Xception: Deep Learning with Depthwise Separable Convolutions

最新推荐文章于 2025-05-10 19:09:36 发布

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本文探讨了Inception模块如何通过独立查看跨通道相关性和空间相关性来提高卷积过程的效率，并介绍了Xception模块的设计理念，即完全解耦跨通道操作与空间操作。实验表明，Xception模块比Inception V3收敛更快且准确率更高。

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Inception series
Conv maps cross-channel correlation and spatial correlation at the same time.
Inception module makes this process easier and more efficient by explicitly factoring it into a series of operations that would independently look at cross-channel correlations and at spatial correlations.
1x1 Conv -> cross-channel correlation; 3x3 & 5x5 Conv -> spatial correlation.
An extreme version of this separation is to entirely decouple the cross-channel and spatial operations, naming Xception.

Xception module:
First use 1x1 Conv
Conduct depthwise separable convolution (DSC): each feature-map have different 3x3 Conv, then concatenate the result of each Conv.
Advantages: Efficient parameter usage
Whole model

Dataset: JET (internal Google dataset), ImageNet, FastEval14k.
Result
- Xception converges faster than Inception V3 and gets higher accuracy.
- 21.0% top-1, 5.5% top-5 error on ImageNet.
- Better with residual connection.
- Worse with non-linear in between the 1x1 and DSC.