Cargando…
A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning
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 clas...
Autores principales: | Jia, Yunpeng, Ye, Xiufen, Liu, Yusong, Xing, Huiming, Guo, Shuxiang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726268/ https://www.ncbi.nlm.nih.gov/pubmed/36483289 http://dx.doi.org/10.1155/2022/6220501 |
Ejemplares similares
-
Feature Selection Methods for Zero-Shot Learning of Neural Activity
por: Caceres, Carlos A., et al.
Publicado: (2017) -
Detecting Errors with Zero-Shot Learning
por: Wu, Xiaoyu, et al.
Publicado: (2022) -
Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval
por: Zhao, Honggang, et al.
Publicado: (2023) -
HFM: A Hybrid Feature Model Based on Conditional Auto Encoders for Zero-Shot Learning
por: Al Machot, Fadi, et al.
Publicado: (2022) -
Semantic-visual shared knowledge graph for zero-shot learning
por: Yu, Beibei, et al.
Publicado: (2023)