FAST OPTIMAL TRANSPORT FOR LATENT DOMAIN ADAPTATION

被引：0

作者：

Roheda, Siddharth ^{[1
]}

Panahi, Ashkan ^{[2
]}

Krim, Hamid ^{[3
]}

机构：

[1] Samsung Res Inst Bangalore, Bangalore, India

[2] Chalmers Univ, Dept Comp Sci & Engn, Gothenburg, Sweden

[3] North Carolina State Univ, Elect & Comp Engn Dept, Raleigh, NC USA

来源：

2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP | 2023年

关键词：

Optimal Transport; Domain Adaptation;

D O I：

10.1109/ICIP49359.2023.10222535

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the target data is unknown. We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.

引用

页码：1810 / 1814

页数：5

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