A method for learning Convolutional Neural Network, CNN, by using a plurality of labeled databases having different label sets, comprising steps of: (a) a learning device establishing (S01) a first to an n-th databases for training, respectively including (i) image data sets by categories, which are obtained by classifying images for a plurality of classes of a plurality of objects by the categories, and (ii) Ground Truth, GT, label sets by the categories, which are generated by checking each of the objects in each of the images included in each of the image data sets and by granting, if each of the objects corresponds to a class belonging to its corresponding category, each information annotated as its corresponding class to each of the objects, wherein each of the GT label sets corresponds to each of the image data sets; (b) the learning device receiving, as at least one input image, at least one specific image belonging to a specific image data set corresponding to a specific class among the n databases for training, and generating (S02) at least one feature map by applying at least one convolution operation to the input image, then generating classification results, by the categories, corresponding to at least one specific object included in the input image based on the feature map, wherein the input image includes the specific object corresponding to the specific class; and (c) the learning device learning (S05) at least one parameter of the CNN by using losses by the categories, which are obtained by referring to the classification results by the categories and their corresponding GT values of the GT label sets by the categories, wherein, at the step of (c), the learning device calculates classification losses by the categories by referring to the classification results and their corresponding GT values of the GT label sets by the categories, to thereby perform backpropagation to learn the parameter of the CNN so that (i) the learning device determines a specific classification result corresponding to a specific GT label set including annotation information on the specific class as the specific class and (ii) the learning device determines one or more other classification results corresponding to one or more other GT label sets not including annotation information on the specific class as a background. (a) a learning device establishing (S01) a first to an n-th databases for training, respectively including (i) image data sets by categories, which are obtained by classifying images for a plurality of classes of a plurality of objects by the categories, and (ii) Ground Truth, GT, label sets by the categories, which are generated by checking each of the objects in each of the images included in each of the image data sets and by granting, if each of the objects corresponds to a class belonging to its corresponding category, each information annotated as its corresponding class to each of the objects, wherein each of the GT label sets corresponds to each of the image data sets; (b) the learning device receiving, as at least one input image, at least one specific image belonging to a specific image data set corresponding to a specific class among the n databases for training, and generating (S02) at least one feature map by applying at least one convolution operation to the input image, then generating classification results, by the categories, corresponding to at least one specific object included in the input image based on the feature map, wherein the input image includes the specific object corresponding to the specific class; and (c) the learning device learning (S05) at least one parameter of the CNN by using losses by the categories, which are obtained by referring to the classification results by the categories and their corresponding GT values of the GT label sets by the categories, <EOS>