Search Results for author:
Found 5 papers, 4 papers with code
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
On Investigating the Conservative Property of Score-Based Generative Models
In addition, we show that USBMs' inability to preserve the property of conservativeness may lead to degraded performance in practice.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks.
Ranked #13 on
Unsupervised Domain Adaptation
on SYNTHIA-to-Cityscapes
Semantic Segmentation Based Unsupervised Domain Adaptation via Pseudo-Label Fusion
In this paper, we propose a pseudo label fusion framework (PLF), a learning framework developed to deal with the domain gap between a source domain and a target domain for performing semantic segmentation based UDA in the unseen target domain.
