Automatic generation of a new class in a classification system

Abstract

A system and computer-implemented method for automatically recognizing a new class in a classification system. The method includes accessing components of a trained convolutional neural network (CNN) that has been trained with available classes. The components are provided in a kernel space and include at least one of a plurality of kernels and a plurality of neurons of one or more layers of the CNN. Furthermore, the components are assigned to a class in accordance with the training. The method further includes applying a covariance matrix to map the components in the kernel space to eigenspace; determining, for each of the available classes, an eigen-distance between a sample and the components mapped to eigenspace; based on the eigen-distance, determining whether the sample is an outlier that does not belong to one of the classes; and creating a new class that includes the sample if determined that the sample is an outlier.