Learning graphons via structured gromov-wasserstein barycenters
We propose a novel and principled method to learn a nonparametric graph model called
graphon, which is defined in an infinite-dimensional space and represents arbitrary-size …
graphon, which is defined in an infinite-dimensional space and represents arbitrary-size …
Modeling Hierarchical Structural Distance for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to estimate a transferable model for unlabeled
target domains by exploiting labeled source data. Optimal Transport (OT) based methods …
target domains by exploiting labeled source data. Optimal Transport (OT) based methods …
Weakly-supervised temporal action alignment driven by unbalanced spectral fused Gromov-Wasserstein distance
Temporal action alignment aims at segmenting videos into clips and tagging each clip with a
textual description, which is an important task of video semantic analysis. Most existing …
textual description, which is an important task of video semantic analysis. Most existing …
Unsupervised domain adaptation via deep hierarchical optimal transport
Unsupervised domain adaptation is a challenging task that aims to estimate a transferable
model for unlabeled target domain by exploiting source labeled data. Optimal Transport (OT) …
model for unlabeled target domain by exploiting source labeled data. Optimal Transport (OT) …
[PDF][PDF] BoMb-OT: On Batch of Mini-batches Optimal Transport
Mini-batch optimal transport (m-OT) has been successfully used in practical applications that
involve probability measures with intractable density, or probability measures with a very …
involve probability measures with intractable density, or probability measures with a very …