4 citations · 6 across the 6 of their papers we have counts for
6 papers
Finding the Most Transferable Tasks for Brain Image Segmentation
Yicong Li, Yang Tan, Jingyun Yang +2
Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source task…
Transferability-Guided Cross-Domain Cross-Task Transfer Learning
Yang Tan, Enming Zhang, Yang Li +2
We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model…
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Jingge Wang, Liyan Xie, Yao Xie +2
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we con…
Transferability Estimation for Semantic Segmentation Task
Yang Tan, Yang Li, Shao-Lun Huang
Transferability estimation is a fundamental problem in transfer learning to predict how good the performance is when transferring a source model (or source task) to a target task.…
Practical Transferability Estimation for Image Classification Tasks
Yang Tan, Yang Li, Shao-Lun Huang
Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transferring a source model (or source task) to a target task. R…
OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations
Yang Tan, Yang Li, Shao-Lun Huang
Transfer learning across heterogeneous data distributions (a.k.a. domains) and distinct tasks is a more general and challenging problem than conventional transfer learning, where e…