一般化
判别式
计算机科学
人工智能
利用
嵌入
域适应
子空间拓扑
领域(数学分析)
适应(眼睛)
机器学习
模式识别(心理学)
数学
数学分析
物理
光学
分类器(UML)
计算机安全
作者
Saeid Motiian,Marco Piccirilli,Donald A. Adjeroh,Gianfranco Doretto
出处
期刊:International Conference on Computer Vision
日期:2017-10-01
被引量:406
标识
DOI:10.1109/iccv.2017.609
摘要
This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high “speed” of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.
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