Open Set Recognition Based on Meta Class Incremental Learning

Traditional image classification algorithms assume that the world is static and closed,whereas the real world is dyna-mic and open,and new categories and their samples are continually emerging,leading to a decrease in the accuracies of traditional image classification algorithms.To address this problem,researchers proposed open set recognition(OSR) problem for the real world which aims at identifying unknown-class samples while maintaining the classification accuracy for known-class samples.However,existing OSR methods generally neglect the further exploitation of identified unknown-class samples and the unknown class samples are scarce in number,so that the classification model is unable to incrementally learn the knowledge of identified unknown class samples,thereby impairing the accuracy and generalization capability of OSR models.Therefore,this paper proposes an OSR method based on meta-incremental learning to improve the accuracy and generalization of OSR models.This method employs a bi-level optimization mechanism to build an OSR model,and then cluster unknown class samples based on deep learning so that the built OSR model can incrementally learn the knowledge of unknown class samples.Specifically,an OSR model based on bi-level optimization mechanism is constructed and trained with few-shot unknown class samples,in order to enable the OSR model to incrementally learn the knowledge of few-shot unknown class samples.Then,a weight excitation attention method is used to obtain the importance of the OSR model's parameters and update non-critical parameters,thereby reducing the impact of incremental learning on the model's ability to classify known-classes.Additionally,a deep learning-based DBSCAN method is designed to extract features and cluster the identified unknown-class samples.Clustered samples are labeled as the same class and performed incremental learning.While samples that are difficult to cluster are rejected,to avoid the impact of too few unknown-class samples on the model's incremental learning effectiveness.Finally,experimental results on four public datasets show that the proposed method outperforms the mainstream open-set recognition methods on AUROC and F1 scores,and adequately learns the knowledge of identified unknown class samples.