.png){kind=icon}
返回
Knowledge distillation-driven semi-supervised multi-view classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2023.102098.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Semi-supervised multi-view classification is a critical research topic that leverages the discrepancy between different views and limited annotated samples for pattern recognition in computer vision. However, it encounters a significant challenge: obtaining comprehensive discriminative representations with a scarcity of labeled samples. Although existing methods aim to learn discriminative features by fusing multi-view information, a significant challenge persists due to the difficulty of transferring complementary information and fusing multiple views with limited supervised information. In response to this challenge, this work introduces an innovative algorithm that integrates Self-Knowledge Distillation (Self-KD) to facilitate semi-supervised multi -view classification. Initially, we employ a view-specific feature extractor for each view to learn discriminative representations. Subsequently, we introduce a self-distillation module to drive information interaction across multiple views, enabling mutual learning and refinement of multi-view unified and specific representations. Moreover, we introduce a class-aware contrastive module to alleviate confirmation bias stemming from noise in the generated pseudo-labels during knowledge distillation. To the best of our knowledge, this is the first attempt to extend Self-KD to address semi-supervised multi-view classification problems. Extensive experimental results validate the efficiency of this approach in semi-supervised multi-view classification compared to existing state-of-the-art methods.
Keyword:
Multi-view learning
Semi-supervised classification
Self-knowledge distillation
Contrastive learning
期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
机构
引用论文
Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification用于图像聚类和半监督分类的自动加权多视图学习
PREAI
分享收藏Joint consensus and diversity for multi-view semi-supervised classification.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning用于多视图半监督学习的可解释图卷积网络
PREAI
分享收藏Generative multi-view and multi-feature learning for classification面向分类的生成式多视图多特征学习INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Ensemble of feature selection algorithms: a multi-criteria decision-making approach特征选择算法的集成: 一种多准则决策方法
PREAI
分享收藏Semi-supervised multi-view maximum entropy discrimination with expectation Laplacian regularizationINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏分享收藏
查看更多内容