Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
This work addresses the challenge of label noise in machine learning, which can degrade model performance, and is incremental as it builds on existing methods like Co-teaching and DivideMix.
The paper tackles the problem of learning with noisy labels by proposing a confidence-based sieving strategy called CONFES to differentiate clean and noisy samples, achieving superior performance in experiments on synthetic and real-world noise settings.
In learning tasks with label noise, improving model robustness against overfitting is a pivotal challenge because the model eventually memorizes labels, including the noisy ones. Identifying the samples with noisy labels and preventing the model from learning them is a promising approach to address this challenge. When training with noisy labels, the per-class confidence scores of the model, represented by the class probabilities, can be reliable criteria for assessing whether the input label is the true label or the corrupted one. In this work, we exploit this observation and propose a novel discriminator metric called confidence error and a sieving strategy called CONFES to differentiate between the clean and noisy samples effectively. We provide theoretical guarantees on the probability of error for our proposed metric. Then, we experimentally illustrate the superior performance of our proposed approach compared to recent studies on various settings, such as synthetic and real-world label noise. Moreover, we show CONFES can be combined with other state-of-the-art approaches, such as Co-teaching and DivideMix to further improve model performance.