Multi-stage ensemble with refinement for noisy labeled data classification

Deep neural networks (DNNs) have made remarkable progress in image classification. However, since DNNs can memorize all the label information in the training dataset due to their excellent feature learning ability, the generalization performance deteriorates when they are trained on the noisy labeled dataset that can be easily found in real-world problems. In this paper, we propose a multi-stage ensemble method with label refinement to build an effective classification model under noisy labels. The proposed method iteratively refines the dataset by re-labeling the samples at the end of each stage, which enables the models trained at each stage to learn different features. By integrating these models, the proposed multi-stage ensemble method exerts powerful generalization performance. Also, we suggest a novel dataset refinement method, demonstrating the effectiveness of a robust function in distinguishing corrupted samples. Experimental results on the benchmark and real-world datasets show that the proposed method outperforms the existing methods on the noisy labeled dataset classification.