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I have seen several different architectures for convolutional neural network (CNN). I am confused which one is the standard and how do I decide what to use. I am not confused by the number of layers being used or the number of parameters involved; I am confused by the COMPONENTS of the network.

Let assume:

    CL = convolution layer SL = subsampling layer(pooling) CM = convolution map NN = neural network Softmax = softmax classifier (similar to linear classifier)

Architecture 1

https://www.youtube.com/watch?v=n6hpQwq7Inw

CL --> SL --> CL --> SL --> CM --> Softmax

Architecture 2 (Do we really need NN at the end again?)

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5605630&tag=1

CL --> SL --> CL --> SL --> NN --> Softmax

Architecture 3

My idea CL--> SL --> CL --> SL --> Softmax

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After in-depth research, there are not a 100% guideline saying that you need to build,e.g., 5 layers of convolution + 2 layers of pooling + 3 layers of fully connected network, in order to achieve good performance.

As papers and competitions result showed (such as ImagNet compeition shows), the deeper the network is (>10 layers), the better the classification is assuming you have enough training data.

For general idea of what components are necessary in a network, you can, for example, read: 2009 What is the best multi-stage architecture for object recognition ny Karrett K., et. al. .

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