Minimum number of samples on a CNN Related to this paper, could it also be applied to convolutional neural networks? I ask this because FNN and CNN share many characteristics
This is the formula:
$(N_e+1)\cdot N_C + (N_C+1)\cdot N_S &lt; 0,1N_{ej}$
Where Ne it is Entry Layers, Nc it is Hidden Layers, Ns it is Output Layers and Nej it is the minimum number of samples needed for training.
CNN is a feed forward neural network that is generally used for Image recognition and object classification. So, the answer it is yes.