Document detail
ID
oai:pubmedcentral.nih.gov:9726...
Topic
Research ArticleAuthor
Jia, Yunpeng Ye, Xiufen Liu, Yusong Xing, Huiming Guo, ShuxiangLangue
enEditor
Hindawi
Category
Computational Intelligence and Neuroscience
Year
2022
listing date
12/12/2022
Keywords
synthesis classes model gzslAbstract
Generalized zero-shot learning (GZSL) aims to classify seen classes and unseen classes that are disjoint simultaneously.
Hybrid approaches based on pseudo-feature synthesis are currently the most popular among GZSL methods.
However, they suffer from problems of negative transfer and low-quality class discriminability, causing poor classification accuracy.
To address them, we propose a novel GZSL method of distinguishable pseudo-feature synthesis (DPFS).
The DPFS model can provide high-quality distinguishable characteristics for both seen and unseen classes.
Firstly, the model is pretrained by a distance prediction loss to avoid overfitting.
Then, the model only selects attributes of similar seen classes and makes sparse representations based on attributes for unseen classes, thereby overcoming negative transfer.
After the model synthesizes pseudo-features for unseen classes, it disposes of the pseudo-feature outliers to improve the class discriminability.
The pseudo-features are fed into a classifier of the model together with features of seen classes for GZSL classification.
Experimental results on four benchmark datasets verify that the proposed DPFS has GZSL classification performance better than that in existing methods.
Jia, Yunpeng,Ye, Xiufen,Liu, Yusong,Xing, Huiming,Guo, Shuxiang, 2022, A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning, Hindawi
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