2、结合半监督学习(Semi-Supervised Learning)的主动学习方法:自训练(Self-training)算法作为半监督学习的一种基础方法,其核心步骤如算法2-3所示。由于自训练算法在训练过程中会根据模型的预测信息,挑选合适的样本及其对应的预测标签加入训练集,而且初始化少量的标注样本能够保证模型的初始性能,因此初始化训练环节对其后续...
Semi-supervised Domain Adaptation Result Code Reference 本文通过首次采用activate learning strategy(AL) 来进行domain adaptation。具体来说,提出Multi-anchor Activate Domain Adaptation (MADA) 方法。 该方法主要包含两个阶段:1)用生成对抗的思想来与训练网络,提出基于多个anchor 的样本选择策略,来选取具有complementary...
This high- lights the importance of domain adaptation for word sense disambiguation... YS Chan,HT Ng - Proc Meeting of the Association for Computational Linguistics 被引量: 158发表: 2007年 Active Learning for Domain Adaptation in the Supervised Classification of Remote Sensing Images This paper pre...
由于半监督学习展示出了优异的性能,在标签不足的情况下,如果能将主动学习与半监督学习结合,将会取得更优异的性能。 【Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels】—https://openaccess.thecvf.com/content/ICCV2021/html/Guo_Semi-Supe...
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been widely studied to exploit the label-rich source data to assist the segmentation of unlabeled samples on target domain. Despite these efforts, UDA performance remains far below that of fully-supervised model owing to the lack of...
Active Supervised Domain Adaptation In this paper, we harness the synergy between two important learning paradigms, namely, active learning and domain adaptation. We show how active learning ... A Saha,P Rai,HD Iii,... - European Conference on Machine Learning & Knowledge Discovery in Databases...
https://openaccess.thecvf.com/content/ICCV2021/html/Guo_Semi-Supervised_Active_Learnin 尽管当前主流方法开始结合 SSL 和 AL(SSL-AL)来挖掘未标记样本的多样化表示,但这些方法的全监督任务模型仍然仅使用标记数据进行训练。此外,这些方法的 SSL-AL 框架存在不匹配问题。在这里,作者提出了一个基于图的 SSL-AL...
we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the ...
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...
https://openaccess.thecvf.com/content/ICCV2021/html/Guo_Semi-Supervised_Active_Learnin 尽管当前主流方法开始结合 SSL 和 AL(SSL-AL)来挖掘未标记样本的多样化表示,但这些方法的全监督任务模型仍然仅使用标记数据进行训练。此外,这些方法的 SSL-AL 框架存在不匹配问题。在这里,作者提出了一个基于图的 SSL-AL...