Can convolutional neural networks be used for small samples?

Convolutional Neural Networks Commonly Understood

Convolutional Neural Networks are commonly understood as follows:

Convolutional Neural Networks (CNNs)-Structure

①CNNN structure generally contains these layers:

Input Layer: used for the input of the data

Convolutional Layer: uses convolution kernel for feature extraction and feature mapping

Excitation Layer: Since convolution is also a linear operation, nonlinear mapping needs to be added

Pooling layer: downsampling is performed to sparse the feature map and reduce the amount of data operations.

Fully Connected Layer: Usually re-fitting in the tail of CNN to reduce the loss of feature information

Output Layer: Used for outputting the result

②There are also some other functional layers in the middle that can be used:

Normalization Layer (BatchNormalization): Normalization of the features in the CNN

Slice Split Layer: separate learning of certain (image) data in separate regions

Fusion Layer: fusion of branches that learn features independently

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Convolutional Neural Networks (CNNs) – Input Layer

1) The input format of the input layer of a CNN preserves the structure of the image itself.

②For a black-and-white 28×28 picture, the input to the CNN is a 28×28 two-dimensional neuron.

3) For a 28×28 picture in RGB format, the input to the CNN is a 3×28×28 three-dimensional neuron (each color channel in RGB has a 28×28 matrix)

2) Convolutional Neural Networks (CNNs)-Convolutional Layer

Feeling Horizons

1) In the Convolutional Layer there are several important concepts:



2 Assuming that the input is a 28×28 two-dimensional neuron, we define 5×5 localreceptivefields, i.e., the neurons in the hidden layer are the same as the 5×5 neurons in the input layer. neurons are connected to the 5×5 neurons in the input layer, and this 5×5 region is called LocalReceptiveFields,