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We also strategically place an additional type of layer, the max pooling layer, between convolutional layers.

Thus, for the deepest network, we can construct an architecture consisting of three convolutional layers followed by a max pooling layer, three more convolutional layers, a second max pooling layer, then the fully connected layers.

When using 1-of-m embedding, a (2 times 2) max pooling layer is placed after every convolutional layer (3 in total), while only a single max pooling layer is included in the network for log-m embedding.

Pooling layer reduces the dimensionality of the input by a constant factor and also undertakes feature selection.

Adding an additional three convolutional layers and one max pooling layer slows training by a factor of 1.20.

The max pooling layer conducts non-linear down sampling by eliminating non-maximal values, thus reducing computation for upper layers as many can be discarded [17].

Each convolutional block is completed by a max-pooling layer (Fig. 5).

A max-pooling layer is subsequently appended between each stage for downsampling the input.

To mitigate overfitting, several advanced structures, such as 1 × 1 convolutional filter and the global average-pooling layer, are carefully introduced in the design of the CNN architecture.

We also avoid to use layers that do not yield visually appealing results in deconvolution, such as the average-pooling layer and the inception layer.

Note that a max-pooling layer only follows the first, second, and fifth convolutional layer but not the third and fourth convolutional layers.