domain adaptation, semi-supervised learning. Recently, some works have demonstrated that consistency regularization can also be utilized in domain adaptation tasks [French et al., 2018]. Although our work is also built upon consistency regularization, rather than regularizing the sample-wise predictions...
Weakly-supervised learning Weakly-supervised learning[47]放松了数据依赖性,这种依赖性要求在强监督下为大量训练数据集提供基本事实标签。有三种类型的弱监督数据:不完整的监督数据、不精确的监督数据和不准确的监督数据。不完整的监督数据意味着仅标记了训练数据的子集。在这种情况下,代表性的方法是SSL和domain adaption。
Semi- Supervised Learning and Domain Adaptation in Natural Language Processing provides a survey of the state of the art, and specific practical information, for anyone confronting a mismatch between data available for training and data encountered at runtime. Three general approaches are discussed: s...
4.5. Semi-Supervised Domain Adaptation Setting In real world applications, we are often provided with pixel-level labels collected from various sources, thus dis- tinct data distributions. To examine the effectiveness of CCT when applied to multiple domains with a var...
Semi-supervised learning for medical application: A survey. In Proceedings of International Conference on Applied Smart Systems, IEEE, Medea, Algeria, pp. 24–25, 2018. DOI: https://doi.org/10.1109/ICASS.2018.8651980. Google Scholar F. Kulwa, C. Li, X. Zhao, B. C. Cai, N. Xu, S...
Application of Semi-Supervised Learning to Graduate Adjusting半监督学习在研究生调剂中的应用半监督学习K-mean算法关联规则聚类Graduate Adjusting is an important ... HUANG ShuCheng,QU YaHui,黄树成,... - 《计算机系统应用》 被引量: 0发表: 2011年 Domain Adaptation via Transfer Component Analysis Domain ...
or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim ...
While we focus on the unsupervised and semi-supervised methods, we will also provide a broader review of other emerging topics, from unsupervised and semi-supervised domain adaptation to the fundamental roles of transformation equivariance and invariance in training a wide spectrum of deep networks. ...
Semantic Segmentation with Active Semi-Supervised Learning Aneesh Rangnekar, Christopher Kanan, Matthew Hoffman Rochester Institute of Technology Rochester, NY, USA aneesh.rangnekar@mail.rit.edu Abstract Using deep learning, we now have the ability to create exceptionally good sema...
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation. [pdf] Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Trevor Darrell, Kurt Keutzer, Han Zhao. CVPR 2021 Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms. [pdf] Yuxing ...