An Efficient Noisy Label Learning Method with Semi-supervised Learning: An Efficient Noisy Label Learning Method with Semi-supervised Learning

Even though deep learning models make success in many application areas, it is well-known that they are vulnerable to data noise. Therefore, researches on a model that detects and removes noisy data or the one that operates robustly against noisy data have been actively conducted. However, most existing approaches have limitations in either that important information could be left out while noisy data are cleaned up or that prior information on the dataset is required while such information may not be easily available. In this paper, we propose an effective semi-supervised learning method with model ensemble and parameter scheduling techniques. Our experiment results show that the proposed method achieves the best accuracy under 20% and 40% noise-ratio conditions. The proposed model is robust to data noise, suffering from only 2.08% of accuracy degradation when the noise ratio increases from 20% to 60% on CIFAR-10. We additionally perform an ablation study to verify net accuracy enhancement by applying one technique after another.