COUQ iteratively combines old-class and novel-class feature reconstruction errors to produce uncertainty scores that stay reliable as new classes arrive, improving continual novelty detection and active learning.
Continual evidential deep learning for out- of-distribution detection
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Uncertainty Quantification in Continual Open-World Learning
COUQ iteratively combines old-class and novel-class feature reconstruction errors to produce uncertainty scores that stay reliable as new classes arrive, improving continual novelty detection and active learning.