View source for Convolutional-Pooling Neural Network (CNN/ConvNet) Model

A [[Convolutional-Pooling Neural Network (CNN/ConvNet) Model]] is a [[multi-layer feed-forward neural network]] that includes [[convolutional layer]]s and [[pooling layer]]s.
* <B>AKA:</B> [[Convolutional-Pooling Neural Network (CNN/ConvNet) Model|Shift Invariant NNet]], [[Convolutional-Pooling Neural Network (CNN/ConvNet) Model|Space Invariant Artificial NNet (SIANN)]].
* <B>Context:</B>
** It can (typically) include [[Convolutional Layer]]s and [[Pooling Layer]]s.
** It can be represented by a [[CNN Architecture]].
** It can be trained by a [[CNN Training System]] (that implements a [[CNN training algorithm]] to solve a [[CNN training task]]).
** It can (often) include [[Normalization Layer]]s and [[Fully-Connected Layer]]s; 
** It can range from being a [[Shallow CNN]] to being a [[Deep CNN]].
** …
* <B>Example(s):</B>
** a [[Temporal Convolutional Network]],
** a [[Convolutional Deep Belief Network (CDBN)]],
** a [[Convolutional Neural Network with Attention Mechanism]],
** a [[Deep Convolutional Neural Network (DCN)]],
** a [[Convolutional Network for Reinforcement Learning]],
** a [[CNN Encoder-Decoder Neural Network]],
** a [[Deep Convolutional Neural Netowrk]] such as: [[VGG Net]], [[AlexNet]], and [[GooLeNet]], 
** an [[Inception Architecture]].
** …
* <B>Counter-Example(s):</B>
** a [[Fully-Connected Neural Network]];
** a [[Convolutional Restricted Boltzmann Machine (CRBM)]];
** a [[Neocognitron Model]];
** an [[HMAX Model]];
** a [[Recurrent Neural Network (RNN)]];
** a [[Recursive Neural Network]];
** a [[Deep Belief Network]];
** a [[Deep Boltzman Machine]];
** a [[Deep Kernel Network]];
** a [[Vision Transformer (ViT)]].
* <B>See:</B> [[Visual Field]], [[Image Recognition]], [[Vanishing Gradient Problem]], [[Bidirectional LSTM-CNN]].

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