Abstract
Robust training with noisy labels is a critical challenge in image
classification, offering the potential to reduce reliance on costly clean-label
datasets. Real-world datasets often contain a mix of in-distribution (ID) and
out-of-distribution (OOD) instance-dependent label noise, a challenge that is
rarely addressed simultaneously by existing methods and is further compounded
by the lack of comprehensive benchmarking datasets. Furthermore, even though
current noisy-label learning approaches attempt to find noisy-label samples
during training, these methods do not aim to estimate ID and OOD noise rates to
promote their effectiveness in the selection of such noisy-label samples, and
they are often represented by inefficient multi-stage learning algorithms. We
propose the Adaptive Estimation of Instance-Dependent In-Distribution and
Out-of-Distribution Label Noise (AEON) approach to address these research gaps.
AEON is an efficient one-stage noisy-label learning methodology that
dynamically estimates instance-dependent ID and OOD label noise rates to
enhance robustness to complex noise settings. Additionally, we introduce a new
benchmark reflecting real-world ID and OOD noise scenarios. Experiments
demonstrate that AEON achieves state-of-the-art performance on both synthetic
and real-world datasets