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Found 4 papers, 2 papers with code
Uncertainty Propagation through Trained Deep Neural Networks Using Factor Graphs
Our implementation balances the benefits of sampling and analytical propagation techniques, which we believe, is a key factor in achieving performance improvements.
Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement
Recent state-of-the-art (SOTA) UDA methods employ a teacher-student self-training approach, where a teacher model is used to generate pseudo-labels for the new data which in turn guide the training process of the student model.
SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
We evaluate SayNav on multi-object navigation (MultiON) task, that requires the agent to utilize a massive amount of human knowledge to efficiently search multiple different objects in an unknown environment.
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
In this regard, source-free domain adaptation (SFDA) excels as access to source data is no longer required during adaptation.
Ranked #4 on
Source-Free Domain Adaptation
on VisDA-2017
