Label Propagation via Teaching-to-Learn and Learning-to-Teach

By Chen Gong, Dacheng Tao, Wei David Liu, Liu Liu, Jie Yang. 2017

136 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Knowledge Distillation: A Survey (2020, background): “It can be practicable to popularize the knowledge transfer to the classic and traditional machine learning methods (Zhou et al., 2019b; Gong et al., 2018; You et al., 2018; Gong et al., 2017).”
Teaching Semi-Supervised Classifier via Generalized Distillation (2018, background): “Other works grounded on manifold assumption include [Wang et al. , 2009; Belkin et al. , 2006; Fang et al. , 2014; Gong et al. , 2015b; Liu et al. , 2017; Gong et al. , 2017a; 2017b ] .”
Learning to Teach with Dynamic Loss Functions (2018, background): “A teaching strategy is designed in [18, 19] to iteratively select unlabeled data to label within the context of multi label propagation, in a similar manner with curriculum learning [8, 28].”
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction (2018, methodology): “Our method is also related to manifold learning [11, 12], more specifically, the discriminative dimensionality reduction [32].”
Learning to Reweight with Deep Interactions (2021, background): “The concept of teaching can be found in label propagation (Gong et al. 2016a,b), pedagogical teaching (Ho et al. 2016; Shafto, Good-man, and Grifﬁths 2014), etc. (Liu et al. 2017) leverages a teaching way to speed up the training, where the teacher model selects the training data balancing the…”
Learning to Teach (2018, methodology): “Some previous research works Gong et al. (2016b; 2017) on multi label propagation were also named with ‘learning to teach’.”
Unifying Graph Convolutional Neural Networks and Label Propagation (2020, background): “…as Label Propagation Algorithm (LPA) (Zhu et al., 2005; Zhou et al., 2004; Zhang & Lee, 2007; Wang & Zhang, 2008; Karasuyama & Mamitsuka, 2013; Gong et al., 2017; Liu et al., 2019a); On the node side V , node features are propagated along edges and transformed through neural network layers,…”
An Overview of Deep Semi-Supervised Learning (2020, unknown): “[74] integrated label-propagation [190, 164, 54] within pseudo-labeling.”
Teacher–Student Curriculum Learning (2017, background): “These, in turn, can be estimated using learned model [33], by assessing noisyness of labels [34], by using multiple input modalities [35], or by using ensemble of models [36].”