本文是为了提供一个用于语义分割的无监督域自适应(Unsupervised domain adaptation)算法的综述。 domain adaptation 的不同层次: input (image) level, internal feature representation level, output level 不同类型:对抗性学习,基于生成,分类器差异分析,自学,熵最小化,课程学习和多任务学习 unsupervised domain adaptatio...
Domain adaptation technology is an effective way to solve this problem, especially unsupervised domain adaptation, which has become a research hotspot in the field of medical image processing because it does not require target domain label information. At present, the...
In this chapter, we first review this work on multi-source source-free unsupervised domain adaptation followed by analysis of a new algorithm which we propose by relaxing some of the assumptions of this prior work. More specifically, instead of naively assuming source data distribution as uniform...
Unsupervised domain adaptation is a popular research topic in the field of classification and detection, and significant progress has been made in semantic segmentation in recent years. Here we provide a brief review of works of unsupervised domain adaptation on natural images and medical images. Most...
Each domain has around 2000 samples and we use features freely available at https://github.com/jindongwang/transferlearning/tree/master/data#amazon-review which follows the data processing pipeline in Chen et al. (201). Each review is preprocessed as a feature vector of unigrams and bigrams ...
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling读书笔记,程序员大本营,技术文章内容聚合第一站。
domainadaptationframeworks.However,thesystemdoes havesomelimitations. For instance, their discrepancy loss (L 1 in this case) is only helpful when the two output proba- bility measures from the classifiers overlap. Inspired by the framework in [58], we focus our ef- ...
We review recent deep learning based domain adaptation methods since they are most related to our proposed method. As the main objective of domain adaptation methods is to learn a representation that is invariant to domain change, we can categorize existing methods into two groups according to the...
We construct 12 cross-domain tasks: B→D, B→E, …, K→E. It should be noted that the Amazon review dataset was already pre-processed into a collection of features (unigrams and bigrams), losing all word order information. This limits the system’s potential performance by forbidding the...
To mitigate the distribution difference between the source and target domains, there have been many unsupervised domain adaptation methods to achieve class-level alignment by aligning the prototypes of two domains. Since the labels of the target domain are unobserved, the target prototypes are construct...