Title: AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Label noise, Noise Classification, Open-Set, Close-Set

Abstract: Robust training with noisy labels is a critical challenge in classification because it offers 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, which is a challenge that is rarely addressed simultaneously by existing methods and is further compounded by the lack of comprehensive benchmarking datasets. Furthermore, many noisy-label learning methods rely on inefficient multistage learning algorithms that explicitly identify noisy samples, which are treated differently from those presumed to be clean. However, this identification typically depends on either oversimplified two-component clustering assumptions (clean vs. noisy) or the arbitrary specification of a noise rate.
To address these research gaps, we propose the Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise (AEON) method. The AEON is an efficient one-stage noisy-label learning methodology that adaptively estimates instance-dependent (ID and OOD) label noise rates to enhance robustness in complex noise settings. In addition, we introduced a new benchmark that reflects real-world ID and OOD noise scenarios. Experiments demonstrate that AEON achieves state-of-the-art performance on both synthetic and real-world datasets.

TL;DR: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning