1. A learning method for improving performance of a CNN by using feature up-sampling networks (FUN) included in a learning device, wherein the learning device includes (i) a down-sampling block for reducing a size of an input image; (ii) each of a (1-1)-th to a (1-k)-th filter blocks from which each of a (1-1)-th to a (1-k)-th feature maps is acquired by performing one or more convolution operations; (iii) a (2-k)-th to a (2-1)-th up-sampling blocks each of which correspondingly interacts with each of the (1-1)-th to the (1-k)-th filter blocks and thereby generates each of a (2-k)-th to a (2-1)-th feature maps; (iv) an application block for generating an application-specific output by using at least part of the (2-k)-th to the (2-1)-th feature maps; and (v) an application-specific loss block for computing a loss by comparing between the application-specific output generated by the application block and Ground Truth (GT), comprising steps of: (a) the learning device, if the input image is obtained, allowing the down-sampling block to acquire a down-sampling image by applying a predetermined operation to the input image for reducing the size thereof; (b) the learning device, if the down-sampling image is obtained, allowing each of the (1-1)-th to the (1-k)-th filter blocks to respectively acquire each of the (1-1)-th to the (1-k)-th feature maps by applying the one or more convolution operations to the down-sampling image; (c) the learning device (I) allowing the (2-1)-th up-sampling block to (i) receive the down-sampling image from the down-sampling block, and (ii) receive the (2-2)-th feature map from the (2-2)-th up-sampling block, and then rescale a size of the (2-2)-th feature map to be identical to that of the down-sampling image and (iii) apply a certain operation to the down-sampling image and the (2-2)-th resealed feature map, thereby acquiring the (2-1)-th feature map and (II) allowing the (2-(M+1))-th up-sampling block to (i) receive the (1-M)-th feature map from the (1-M)-th filter block, and (ii) receive the (2-(M+2))-th feature map from the (2-(M+2))-th up-sampling block, and then rescale a size of the (2-(M+2))-th feature map to be identical to that of the (1-M)-th feature map and (iii) apply a certain operation to the (1-M)-th feature map and the (2-(M+2))-th resealed feature map in order to generate the (2-(M+1))-th feature map, thereby acquiring the (2-k)-th to the (2-2)-th feature maps, wherein M is an integer greater than or equal to 1; and (d) the learning device (i) allowing the application block to acquire the application-specific output by applying an application-specific operation to at least part of the (2-k)-th to the (2-1)-th feature maps and (ii) allowing the application-specific loss block to acquire the loss by comparing the application-specific output to the GT; and thereby adjusting parameters of at least part of the application block, the (2-k)-th to the (2-1)-th up-sampling blocks and the (1-1)-th to the (1-k)-th filter blocks by using the loss during a first backpropagation process.