LEARNING METHOD AND LEARNING DEVICE USING MULTIPLE LABELED DATABASES WITH DIFFERENT LABEL SETS AND TESTING METHOD AND TESTING DEVICE USING THE SAME

摘要

The present invention relates to a method for learning a convolutional neural network (CNN) by using a plurality of labeled databases having different label sets. The method includes the steps of a learning device: (a) establishing databases for training, respectively including image data sets by category, and GT label sets by category, if each of the objects corresponds to a class belonging to its corresponding category, each information annotated as its corresponding class to the object, wherein the GT label sets correspond to the image data sets; (b) receiving, as an input image, a specific image belonging to a specific image data set corresponding a specific class among the databases for training, generating a feature map, and then generating classification results, by category, corresponding to a specific object included in the input image based on the feature map; and (c) learning parameters of the CNN by using losses by category.