In this work, we focus on the rigorous OT modeling for conditional distribution matching and label shift correction. A novel masked OT (MOT) methodology on ...
In this work, we focus on the rigorous OT modeling for conditional distribution matching and label shift correction. A novel masked OT (MOT) methodology on ...
As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models ...
There are two major parameters for MOT model, i.e., entropic regularization parameter λ and relaxation parameter β for. UOT. For sensitivity of parameters, the ...
A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information. Paper
As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models ...
May 20, 2023 · A novel OT methodology to deal with extreme learning scenarios. Video presentation of CVPR 2023 paper "MOT: Masked Optimal Transport for ...
A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information. Partial Domain Adaptation.
This repo is a collection of AWESOME things about Optimal Transport in Deep Learning, including useful materials, papers, code.
A novel OT methodology to deal with extreme learning scenarios. Video presentation of CVPR 2023 paper "MOT: Masked Optimal Transport for Partial Domain ...