Liang Mi’s ECCV Paper Got Accepted, Congratulation!
A new clustering method which solves the k-means clustering problem with variational optimal transportation. It leverages power Voronoi diagram to aggregate empirical observations into a fixed number of Voronoi cells while maintaining the minimum transportation cost and preliminary results have shown its applications in domain adaptation, remeshing, and representation learning.
![Image of Liang Mi's ECCV Paper: A new clustering method which solves the k-means clustering problem with variational optimal transportation. It leverages power Voronoi diagram to aggregate empirical observations into a fixed number of Voronoi cells while maintaining the minimum transportation cost and preliminary results have shown its applications in domain adaptation, remeshing, and representation learning.](https://gsl.lab.asu.edu/wp-content/uploads/sites/117/2022/05/liangeccv2019-1-1024x458.png)