.png){kind=icon}
返回
Unsupervised Domain Adaptation Based on Correlation Maximization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3111586.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This research proposes a novel unsupervised domain adaptation algorithm for cross-domain visual recognition. Distance Correlation-based Domain Adaptation or DCDA algorithm is developed by a correlation measure, called distance correlation. DCDA exploits both the statistical and geometrical properties of the data while embedding the both domain instances to a latent feature space. Unlike many proposed algorithms in the literature that utilize the source domain labels to learn pseudo labels, DCDA further exploits the available information in the source domain labels to discover an appropriate projection operator. The implementation of the proposed DCDA algorithm is easy, and it has a closed-form solution. Our experiments and analyses of the results over a wide variety of benchmark domain adaptation data sets indicate that DCDA has significantly better results in comparison with other state-of-the-art approaches in unsupervised domain adaptation and deep learning literature.
Keyword:
Task analysis
Correlation
Linear programming
Kernel
Classification algorithms
Training
Prediction algorithms
Transfer learning
unsupervised domain adaptation (UDA)
distance correlation (dCor)
maximum mean discrepancy (MMD)
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Autologous graft-versus-host disease: harnessing anti-tumor immunity through impaired self-tolerance
PREAI
分享收藏Diffusion of nickel and surface reconstruction in Ni/TiO2 catalysts promoted by H2 and O2 treatments
PREAI
分享收藏
The roles of actin cytoskeleton and microtubules for membrane recycling of a food vacuole in Tetrahymena thermophila 嗜热四膜虫 中肌动蛋白细胞骨架和微管在食品液泡膜循环中的作用
PREAI
分享收藏Visual domain adaptation based on modified A-distance and sparse filtering.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏
查看更多内容