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It has been a long I am confused on understanding some of the AlexNet architecture : enter image description here

The output of the first conv layer is 55x55x48 (96 considering the division between GPUs but let's stick to 1 GPU so depth 48). Then max pooling is applied and there come my problem.

When applying max pooling, the result is 27x27x48 right ? If so, how is applied the next convolution over this result (with 5x5x48 filters) to output 27x27x128 ? I finally don't see how and when to apply max-pooling in between convolutions. I must miss something here...

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Okay, I got it. If anyone interested, they use 5x5 filter but with padding 2 and striding 1 so that with bias it doesn't change the 2D dimension of the output when applied on the result of max-pooling. The info on padding isn't present on the original paper...

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It uses same padding which means the output of max-pooling is padded with zeros in a way that the output of next layer preserves the width and height. for information take a look at here.

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  • $\begingroup$ If the padding was 0, the output size would be (27-5)/1 + 1 = 23x23x128, but it remains 27x27, how would it be possible then ? $\endgroup$
    – Elliot
    Dec 5, 2017 at 16:06
  • $\begingroup$ but padding is not zero. I have referred that it is same so it preserves the height and width. do you know what is same padding? $\endgroup$
    – Green Falcon
    Dec 5, 2017 at 16:20
  • $\begingroup$ I understood "zero padding", my fault. Same padding is exactly what I said below, padding of size 2 in that precise case. Thanks $\endgroup$
    – Elliot
    Dec 5, 2017 at 16:24

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