Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

@article{Ge2021EmbeddingSH,
  title={Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization},
  author={Yubin Ge and Site Li and Xuyang Li and Fangfang Fan and Wanqing Xie and Jane Jia You and Xiaofeng Liu},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2021},
  pages={2835-2839}
}
  • Yubin Ge, Site Li, +4 authors Xiaofeng Liu
  • Published 30 April 2021
  • Computer Science
  • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this paper, we propose to incorporate the risk-aware inter-class correlation in a discrete optimal transport (DOT) training framework by configuring its ground distance matrix. The ground… 
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