Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation

• URL: http://arxiv.org/abs/2305.19486v3
• Date: Fri, 5 Jul 2024 00:15:02 GMT
• Title: Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
• Authors: Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro,
• Abstract summary: Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples. Such curriculum is sub-optimal since it does not consider the actual label noise rate in the training set. This paper addresses this issue with a new noise-rate estimation method that is easily integrated with most state-of-the-art (SOTA) LNL methods.
• Score: 16.283722126438125
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 presence of instance-dependent noise (IDN), a realistic form of label noise arising from ambiguous sample information. To address IDN, Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples. This stage uses an arbitrary criterion and a pre-defined curriculum that initially selects most samples as noisy and gradually decreases this selection rate during training. Such curriculum is sub-optimal since it does not consider the actual label noise rate in the training set. This paper addresses this issue with a new noise-rate estimation method that is easily integrated with most state-of-the-art (SOTA) LNL methods to produce a more effective curriculum. Synthetic and real-world benchmark results demonstrate that integrating our approach with SOTA LNL methods improves accuracy in most cases.

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