# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 1 Introduction  
Deep neural networks (DNNs) have witnessed great applications in various fields, enabling remarkable advancements in supervision-based classification tasks, due to the existence of a huge volume of training data with correct labels. However, practically, real-world datasets often face the problem of the high cost of manual labeling and even disagreement among experts in specialized fields [1], which always make the labels of training samples noisy, i.e., not sufficiently correct. The ubiquitous presence of noisy labels in real-world training data poses a significant challenge: DNNs are capable of memorizing the entire data, which easily overfits noisy labels and leads to poor performance duringinference [2]. Therefore, it is particularly important to improve the accuracy and robustness of deep learning models, when facing noisy labels in training data [3; 4]. Existing methods for learning with noisy labels (LNLs), such as the noise transition matrix [5], mainly focus on modeling noisy data. However, in practical applications, the noise types are usually dependent on the features of the samples. Thus, it is difficult to estimate the noise transition matrix, especially when the number of classes is large [6]. Therefore, instead of modeling noisy labels, based on the observation that DNNs often tend to fit samples with clean labels, i.e., learning simple patterns before fitting noisy labels, recent research such as MentorNet [7] aims to select clean samples out of the noisy ones and then uses them to update the DNN classifier through treating low-loss samples as clean [8]. To alleviate the accumulated error caused by the biased selection of training samples, co-teaching [9] maintains two neural networks simultaneously, in which each network feeds its filtered small-loss samples to teach its peer network for updating the parameters. However, the drawback of the above sample-selection-based methods lies in that they only use filtered clean samples for training and overlook training subsets which may affect performance in high-noise scenarios. Unlike approaches that discard samples with noisy labels, meta-learning methods assign adaptive weights to each sample, with noisier ones receiving lower weights. Specifically, MetaLabelNet [10] uses a portion of existing clean samples in each training batch to train a classifier, which is then used to generate soft labels for other samples to train the base classifier. However, directly training the model on clean data may cause it to overfit, resulting in poor performance on the test dataset. Different from MetaLabelNet, with the help of a partially clean dataset, Learning to Bootstrap (L2B) [11] relabels training samples and further reweights different loss terms. However, the potentially biased relabeling of samples in L2B may lead to accumulative errors and consequently limit the performance of the model. In brief, unlike previous approaches that directly utilize clean data to generate pseudo-labels or rely only on single models to reweight samples [10; 11; 12], we propose a novel classifier framework robust to a high noise ratio, called a cross dual-branch network guided by meta-data on a single side (CoNet-MS), which fully exploits the DNN training characteristics and prevents the accumulation of errors under a meta-learning framework. Specifically, our contribution can be summarized as follows:  
* We propose a cross dual-branch classifier framework enhanced with meta-learning to guide each other and achieve adaptive sample relabeling, CoNet-MS. CoNet-MS can improve the accuracy of classifiers by cross-guiding dual-branch and reweighting labels with the meta-guided module on the upper branch. Two classifier networks with divergent initialization can facilitate learning complementary features to filter distinct errors from noisy labels. Following the small-loss principle [13] where DNNs prioritize clean samples, each branch treats low-loss samples as high-confidence pseudo-clean ones.