Multi-label Classification via Feature-aware Implicit Label Space Encoding

By Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang. 2014

126 citations recorded by the source corpus.

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Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “Alternative solutions to capture label co-occurrences include learning joint input-label embeddings with rankingbased losses [66, 31, 70, 29, 15], graph neural networks [8], and using loss functions that directly account for those [15, 63, 42, 54].”
Inverse Cooking: Recipe Generation From Food Images (2018, background): “Moreover, joint input and label embeddings [57, 25, 61] have been introduced to preserve correlations and predict label sets.”
Multi-label Zero-Shot Learning with Structured Knowledge Graphs (2017, background): “Label embedding methods are among the popular techniques, which transform labels into embedded label vectors, so that the correlation between labels can be exploited [47, 43, 18, 29, 49].”
Sparse Local Embeddings for Extreme Multi-label Classification (2015, background): “[14] Z.”
Learning Deep Latent Spaces for Multi-Label Classification (2017, methodology): “…is also a popular technique for multi-label classiﬁcation (Balasubramanian and Lebanon 2012; Tai and Lin 2012; Chen and Lin 2012; Bi and Kwok 2013; Hsu et al. 2009; Zhang and Schneider 2012; Zhou, Tao, and Wu 2012; Lin et al. 2014; Yu et al. 2014; Li and Guo 2015; Bhatia et al. 2015).”
Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016, background): “Extreme multi-label learning algorithms typically follow a tree [3, 33, 45] or an embedding based approach [4, 5, 6, 9, 10, 13, 17, 19, 21, 26, 31, 35, 42, 44, 47, 49].”