Performance Boosting Mislabels Correction with Semi-Supervised Learning and Deep Feature Similarity Measurements

In this paper, dataset is applied from the perspective of semi-supervised learning, using a small amount of clean annotated data and combining a large amount of misannotated data for training. Clothing1M was used in the experiments. Therefore, the purpose of this study is to tackle the problem of noisy datasets to boost the models' performance. From the perspective of semi-supervised learning, the clean dataset is treated as the labeled dataset, and the remaining noisy data are regarded as the unlabeled data. The initial model was trained on the labeled dataset first, and then the model was used to perform feature extraction on the unlabeled dataset. The "prototypes" for each category can be obtained via feature matching and clustering. As a result, the dual screening scheme is proposed to take the model's predictions and the predictions from the prototypes method into account, reducing the impact of noisy data. The clean dataset after screening and the remaining data with noisy labels were trained by MixMatch to further enhance the robustness of models. Experimental results show that the proposed methods can boost the classification performance by 3% in accuracy, and outperform the state-of-the-art method by 1%. It achieves (1) cost reduction in labeling, (2) impact mitigation of noisy data via the dual screening scheme, and (3) performance boosting by semi-supervised learning. -c 2023 Society for Imaging Science and Technology.