.png){kind=icon}
返回
Unsupervised domain adaptation for regression using dictionary learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2023.110439.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Unsupervised domain adaptation aims to generalize the knowledge learned on a labeled source domain across an unlabeled target domain. Most of existing unsupervised approaches are feature-based methods that seek to find domain invariant features. Despite their wide applications, these approaches proved to have some limitations especially in regression tasks. In this paper, we study the problem of unsupervised domain adaptation for regression tasks. We highlight the obstacles faced in regression compared to a classification task in terms of sensitivity to the scattering of data in feature space. We take this issue and propose a new unsupervised domain adaptation approach based on dictionary learning. We seek to learn a dictionary on source data and follow an optimal direction trajectory to minimize the residue of the reconstruction of the target data with the same dictionary. For stable training of a neural network, we provide a robust implementation of a projected gradient descent dictionary learning framework, which allows to have a backpropagation friendly end-to-end method. Experimental results show that the proposed method outperforms significantly most of state-of-the-art methods on several well-known benchmark datasets, especially when transferring knowledge from synthetic to real domains.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Domain adaptation
Transfer learning
Regression
Deep learning
Dictionary learning
Sparse coding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
Long Range Sequencing Shows Improved Resolution in the Detection of Beta Globin Cluster Variants.png)
Blood
OAAI
BloodIF0
OAAI分享收藏Entropy-weighted reconstruction adversary and curriculum pseudo labeling for domain adaptation in semantic segmentationNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏
查看更多内容