Semi-Supervised Multi-Label Learning with Incomplete Labels
By Feipeng Zhao, Yuhong Guo. 2015
60 citations recorded by the source corpus.
Subfields: Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Representation & Self-Supervised Learning
- Adriana Romero
- Mingli Song
- Weixin Liang
- Dapeng Tao
- Yuncheng Li
- Yale Song
- Amaia Salvador
- Michal Drozdzal
Citations in the corpus
- Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “Multi-label classification has been a long lasting problem in computer vision [58, 16, 59, 54].”
- Improving Pairwise Ranking for Multi-label Image Classification (2017, methodology): “We propose a new pairwise ranking loss function and a per-class threshold estimation method in a unified framework, improving existing ranking-based approaches in a principled manner. label dependency [1, 25], label sparsity [10, 12, 27], and label noise [33, 39].”
- CU-Net: Component Unmixing Network for Textile Fiber Identification (2019, background): “…2014, 2018; Zhou and Tao 2012; Zhang et al. 2015), label dependency (Shu et al. 2015; Alessandro et al. 2013; Liu and Tsang 2015; Verma and Jawahar 2016; Zhang and Zhou 2007; Zhou et al. 2012; Wu et al. 2018), and label noise (Wu et al. 2015a; Zhao and Guo 2015; Xu et al. 2015; Bucak et al. 2009).”