A Dual Discriminator Method for Generalized Zero-Shot Learning  

作　　者：Tianshu Wei Jinjie Huang 

机构地区：[1]School of Computer Science and Technology,Harbin University of Science and Technology,Harbin,150006,China [2]School of Automation,Harbin University of Science and Technology,Harbin,150006,China

出　　处：《Computers, Materials & Continua》2024年第4期1599-1612,共14页计算机、材料和连续体（英文）

摘　　要：Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.

关 键 词：Generalized zero-shot learning modality consistent DISCRIMINATOR domain shift problem feature fusion 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]