Abstract: A method of generating a classifier by using a small number of labeled images includes: pre-training a wide residual network by using a set of labeled data with a data amount meeting requirements, and determining portions of the pre-trained wide residual network except for a fully connected layer as a feature extractor for an image; randomly selecting, for a N-class classifier to be generated, N classes from a training set for each of a plurality of times; and for N classes selected each time: randomly selecting one or more images from each class of the N classes as training samples; extracting a feature vector from training samples of each class by using the feature extractor; inputting a total of N feature vectors extracted into a classifier generator; and sequentially performing a class information fusion and a parameter prediction for the N-class classifier by using the classifier generator.