Knowledge transduction for cross-domain few-shot learning

Pengfang Li; Fang Liu; Licheng Jiao; Shuo Li; Lingling Li; Xu Liu; Xinyan Huang

doi:10.1016/j.patcog.2023.109652

ScienceGate Book Chapters

JOURNAL ARTICLE

Knowledge transduction for cross-domain few-shot learning

Pengfang Li Fang Liu Licheng Jiao Shuo Li Lingling Li Xu Liu Xinyan Huang

Year: 2023 Journal: Pattern Recognition Vol: 141 Pages: 109652-109652 Publisher: Elsevier BV

DOI: 10.1016/j.patcog.2023.109652

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Transduction (biophysics) Artificial intelligence Feature (linguistics) Domain (mathematical analysis) Domain adaptation Representation (politics) Consistency (knowledge bases) Pattern recognition (psychology) Feature learning Domain knowledge Semantics (computer science) Class (philosophy) Machine learning Mathematics

Metrics

Cited By

13.28

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Knowledge transduction for cross-domain few-shot learning

Metrics

Citation History

Topics

Related Documents

Target Guided Knowledge Distillation for Cross-Domain Few-Shot Learning

Knowledge-guided distribution alignment for cross-domain few-shot learning

Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer

Hyperbolic Insights With Knowledge Distillation for Cross-Domain Few-Shot Learning

Enhancing few-shot lifelong learning through fusion of cross-domain knowledge