The ConvoluTIonal Neural Network (CNN) is a feedforward neural network whose artificial neurons can respond to a surrounding area of ​​a part of the coverage and perform well for large image processing. It includes an alternaTIng convoluTIonal layer and a pooling layer.

Convolutional neural networks are an efficient method of identification that has developed in recent years and has attracted widespread attention. In the 1960s, when Hubel and Wiesel studied the local sensitive and directional selection of neurons in the cat's cerebral cortex, they found that their unique network structure can effectively reduce the complexity of the feedback neural network, and then proposed a convolutional neural network ( ConvoluTIonal Neural Networks - CNN for short). Nowadays, CNN has become one of the research hotspots in many scientific fields, especially in the field of pattern classification. Because the network avoids the complicated pre-processing of images, it can directly input the original image, so it has been widely used. The new recognition machine proposed by K. Fukushima in 1980 is the first implementation network of convolutional neural networks. Subsequently, more researchers have improved the network. Among them, representative research results are the “improvement of cognitive machines” proposed by Alexander and Taylor, which combines the advantages of various improved methods and avoids time-consuming error back propagation.

Generally, the basic structure of the CNN includes two layers, one of which is a feature extraction layer, and the input of each neuron is connected to the local acceptance domain of the previous layer, and the local features are extracted. Once the local feature is extracted, its positional relationship with other features is also determined; the second is the feature mapping layer, each computing layer of the network is composed of multiple feature maps, and each feature map is a plane. All neurons in the plane have equal weights. The feature mapping structure uses a small sigmoid function that affects the function kernel as the activation function of the convolutional network, so that the feature map has displacement invariance. In addition, since the neurons on one mapping surface share weights, the number of network free parameters is reduced. Each convolutional layer in the convolutional neural network is followed by a computational layer for local averaging and quadratic extraction. This unique two-feature extraction structure reduces feature resolution.

CNN is mainly used to identify two-dimensional graphics of displacement, scaling and other forms of distortion invariance. Since the feature detection layer of the CNN learns through the training data, when the CNN is used, the feature extraction of the display is avoided, and the learning data is implicitly learned from the training data; and the weights of the neurons on the same feature mapping surface are the same. So the network can learn in parallel, which is also a big advantage of the convolutional network relative to the neural network connected to each other. Convolutional neural networks have unique advantages in speech recognition and image processing due to their special structure of local weight sharing. Their layout is closer to the actual biological neural network, and weight sharing reduces the complexity of the network, especially multidimensional. The feature that the input vector image can be directly input into the network avoids the complexity of data reconstruction during feature extraction and classification.
To give you a summary of the various operations of the convolutional neural network, everyone can understand!

First come a few static images:

Some magical GIF animations of convolutional algorithms, including different padding and strides.

The above are four different convolution methods. Do you know which kind of convolution? Welcome to the comments section to give the answer!

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