no code implementations • 19 Jun 2023 • Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso
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.
1 code implementation • 10 Mar 2023 • Luca Franco, Paolo Mandica, Bharti Munjal, Fabio Galasso
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