Distilling Inter-Class Distance for Semantic Segmentation

no code implementations • 7 May 2022 • Zhengbo Zhang, Chunluan Zhou, Zhigang Tu

Knowledge distillation is widely adopted in semantic segmentation to reduce the computation cost. The previous knowledge distillation methods for semantic segmentation focus on pixel-wise feature alignment and intra-class feature variation distillation, neglecting to transfer the knowledge of the inter-class distance in the feature space, which is important for semantic segmentation.

Knowledge Distillation Position +2

Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation

no code implementations • 28 Jan 2020 • Xiaoli Liu, Pan Hu, Zhi Mao, Po-Chih Kuo, Peiyao Li, Chao Liu, Jie Hu, Deyu Li, Desen Cao, Roger G. Mark, Leo Anthony Celi, Zhengbo Zhang, Feihu Zhou

This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.

Interpretable Machine Learning Mortality Prediction +1