Learning method, learning device with multi-feeding layers and testing method, testing device using the same

Abstract

A learning method for a CNN (Convolutional Neural Network) capable of encoding at least one training image with multiple feeding layers, wherein the CNN includes a 1st to an n-th convolutional layers, which respectively generate a 1st to an n-th main feature maps by applying convolution operations to the training image, and a 1st to an h-th feeding layers respectively corresponding to h convolutional layers (1≤h≤n−1)) is provided. The learning method includes steps of: a learning device instructing the convolutional layers to generate the 1st to the n-th main feature maps, wherein the learning device instructs a k-th convolutional layer to acquire a (k−1)-th main feature map and an m-th sub feature map, and to generate a k-th main feature map by applying the convolution operations to the (k−1)-th integrated feature map generated by integrating the (k−1)-th main feature map and the m-th sub feature map.