AEON jointly estimates closed-set and open-set label noise rates via learnable parameters and uses them to adaptively weight samples in one-stage training, achieving state-of-the-art accuracy on several noisy-label benchmarks.
Embedding con- trastive unsupervised features to cluster in- and out-of-distribution noise in corrupted 15 image datasets
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
AEON jointly estimates closed-set and open-set label noise rates via learnable parameters and uses them to adaptively weight samples in one-stage training, achieving state-of-the-art accuracy on several noisy-label benchmarks.