Abstract: A method and device for labeling are provided. A labeling method includes: determining inference performance features of respective neural network models included in an ensemble model, wherein the inference performance features correspond to performance of the neural network models with respect to inferring classes of the ensemble model; based on the inference performance features, determining weights for each of the classes for each of the neural network models, wherein the weights are not weights of nodes of the neural network models; generating classification result data by performing a classification inference operation on labeling target inputs by the neural network models; determining score data representing confidences for each of the classes for the labeling target inputs by applying weights of the weight data to the classification result data; and measuring classification accuracy of the classification operation for the labeling target inputs based on the score data.

Abstract: A processor-implemented method includes: generating a first sample image and a second sample image by performing data augmentation on an input training image; generating a first feature map of the first sample image and a second feature map of the second sample image by performing feature extraction on the first sample image and the second sample image using an encoding model; determining first loss data according to a relationship between first feature vectors of the first feature map and second feature vectors of the second feature map; estimating relative geometric information of the first feature map and the second feature map using a relationship estimation model; determining second loss data according to the relative geometric information, based on label data according to a geometric arrangement of the first sample image and the second sample image in the input training image; and training the encoding model and the relationship estimation model, based on the first loss data and the second loss data.