Related Experiment Video
Updated: May 1, 2026
03:31
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
537
Ensemble Transductive Propagation Network for Semi-Supervised Few-Shot Learning.
Xueling Pan1,2, Guohe Li1,2, Yifeng Zheng3,4
1Beijing Key Lab of Petroleum Data Mining, Department of Geophysics, China University of Petroleum, Beijing 102249, China.
Entropy (Basel, Switzerland)
|February 23, 2024
Summary
This study introduces ensemble transductive propagation networks (ETPN), a novel strategy for few-shot learning that leverages unlabeled data and Dempster-Shafer (D-S) evidence fusion. ETPN enhances model accuracy and stability, outperforming existing few-shot learning methods.
Area of Science:
- Machine Learning
- Computer Vision
- Artificial Intelligence
Background:
- Few-shot learning faces challenges with limited training data, leading to high variance, bias, and overfitting.
- Graph-based transductive few-shot learning utilizes unlabeled data to improve predictions, emerging as a significant research area.
- Existing methods struggle to effectively integrate information from limited labeled and abundant unlabeled data.
Purpose of the Study:
- To propose a novel ensemble semi-supervised few-shot learning strategy named ensemble transductive propagation networks (ETPN).
- To enhance the utilization of unlabeled data and improve the stability and accuracy of few-shot learning models.
- To address the limitations of current few-shot learning approaches by integrating transductive networks and Dempster-Shafer evidence fusion.
Main Methods:
- Developed homogeneity and heterogeneity ensemble transductive propagation networks with preset weight coefficients for iterative inference.
- Improved Dempster-Shafer (D-S) evidence fusion by incorporating information entropy for stable multi-model result fusion.
- Enhanced ensemble pruning using L2 norm to select accurate individual learners and introduced interference sets for improved anti-disturbance capabilities.
Main Results:
- The proposed ensemble transductive propagation networks (ETPN) demonstrated superior performance compared to state-of-the-art few-shot learning models.
- Achieved accuracy improvements of 0.3% and 0.28% in 5-way 5-shot settings on miniImagNet and tieredImageNet, respectively.
- Showcased significant gains of 3.43% and 7.6% in 5-way 1-shot settings on miniImagNet and tieredImageNet, highlighting effectiveness with extremely limited data.
Conclusions:
- ETPN effectively addresses the challenges of few-shot learning by robustly utilizing unlabeled data through transductive learning and evidence fusion.
- The novel integration of ensemble methods, D-S theory, and transductive propagation offers a stable and accurate approach for few-shot classification.
- The proposed strategy represents a significant advancement in few-shot learning, particularly in scenarios with scarce labeled samples.
