Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label

Deep neural network (DNN)-based classifiers have witnessed great applications in various fields. Unfortunately, the labels of real-world training data are commonly noisy, i.e., the labels of a large percentage of training samples are wrong, which negatively affects the performance of a trained DNN classifier during inference. Therefore, it is challenging to practically formulate a robust DNN classifier using noisy labels in training. To address the above issue, our work designs an effective architecture for training the robust DNN classifier with noisy labels, named a cross dual-branch network guided by meta-data on a single side (CoNet-MS), in which a small amount of clean data, i.e., meta-data, are used to guide the training of the DNN classifier. Specifically, the contributions of our work are threefold. First, based on the principle of small loss, each branch using the base classifier as a neural network module infers partial samples with pseudo-clean labels, which are then used for training another branch through a cross structure that can alleviate the cumulative impact of mis-inference. Second, a meta-guided module is designed and inserted into the single branch, e.g., the upper branch, which dynamically adjusts the ratio between the observed label and the pseudo-label output by the classifier in the loss function for each training sample. The asymmetric dual-branch design makes two classifiers diverge, which facilitates them to filter different types of noisy labels and avoid confirmation bias in self-training. Finally, thorough experiments demonstrate that the trained classifier with our proposal is more robust: the accuracy of the classifier trained with our proposed CoNet-MS on multiple datasets under various ratios of noisy labels and noise types outperforms other classifiers of learning with noisy labels (LNLs), including the state-of-the-art meta-data-based LNL classifier.