3 papers
cs.CV2025
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
Arpit Garg, Cuong Nguyen, Rafael Felix +3
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 oft…
cs.CV2024
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presen…
cs.CV2024
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
Arpit Garg, Cuong Nguyen, Rafael Felix +2
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with no…