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In the inception networks like inception-v3 and inception-v4, the kernel sizes are smaller in the lower layers,such as 3*3, but in the higher layers, the kernel sizes seem to be larger,such as 5*5,7*7,although they may be factorized to n*1&1*n later.But as network goes deeper,the spatial size of the feature map goes down,is there any relationship between these two thing?

ps:My question is why the kernel sizes in the lower layers seem to be samller(no more than 3*3),and you can find larger kernel size like 7*7 in the higher layers(more precisely, in the middle layers ).Is there any relationship between the spatial size of the feature map and the spatial spatial size of the conv kernels?Take inception v3 as a example, when the spatial sizes of the feature maps are larger than 35 in the first few layers of the network,the biggest kernel size is 5*5, but when the spatial size become 17, kernel size like 7*7 is used.

Any help will be appreciated.

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