.png){kind=icon}
返回
Contrastive learning based open-set recognition with unknown score
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2024.111926.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Open -set recognition (OSR) is designed to classify the seen classes and identify the unseen classes as unknown. However, existing open -set classifiers rely on deep networks trained using supervised learning techniques for known classes present in the training samples. This factor tends to specialize the learned features of the model toward known classes, making it challenging to differentiate between unknown classes. In this study, we propose a novel open -set recognition framework based on contrastive learning with an unknown score. First, we propose a novel training framework based on contrastive learning to learn more informative features and preserve beneficial information to separate unknown from known. Second, we propose an unknown score function based on multi -layer features to detect unknown samples by considering the difference between the known and unknown classes of intermediate -layer features. Based on extensive experiments conducted on multiple benchmark datasets, the proposed method has been demonstrated to outperform existing methods and achieve state-of-the-art results.
Keyword:
Classification
Open-set recognition
Contrastive learning
Unknown score
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Self-supervised Graph-level Representation Learning with Adversarial Contrastive Learning基于对抗性对比学习的自监督图级表示学习
OAAI
OAAI分享收藏Hierarchical full-attention neural architecture search based on search space compression基于搜索空间压缩的分层全注意力神经结构搜索
PREAI
分享收藏Unsupervised graph-level representation learning with hierarchical contrasts具有层次对比的无监督图级表示学习.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
查看更多内容