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Simple sorting of selective kernel networks methods
2022-07-19 05:48:00 【byzy】
Link to the original text :Selective Kernel Networks | IEEE Conference Publication | IEEE Xplore
For different sizes of input information, adaptive learning convolution kernel size .

It is divided into 3 A link :
split: Transformation of two different convolution kernels ( Successive grouped/depth-wise Convolution +BN+ReLU) Act on respectively On , obtain
and
.
fuse: Add the outputs of the above two transformations to get , Then, the average value is pooled to get
. Pass a layer FC、BN and ReLU, Reduce the dimension to the original
( At the same time, in order to avoid the dimension being too small, set the minimum value
), obtain
Dimension vector
.
select: Using multiple linear layers will After transformation softmax, obtain soft attention vector ( In the figure
). namely
among Is the linear layer weight
Of the
That's ok ,
yes
Of the
Elements ;
Empathy .
Finally, press the output :
The above is the case of two branches . It is easy to expand to multiple branches .
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