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Found 2 papers, 2 papers with code
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
In HALO (Hyperbolic Active Learning Optimization), for the first time, we propose the use of epistemic uncertainty as a data acquisition strategy, following the intuition of selecting data points that are the least known.
Ranked #1 on
Source-Free Domain Adaptation
on GTA5 to Cityscapes
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace.
Ranked #61 on
Skeleton Based Action Recognition
on NTU RGB+D 120
