# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 2 Related Works  
This section introduces the related works of LNLs in the field of deep learning, which is categorized as schemes directly learning with noisy labels and meta-learning-assisted learning with noisy labels. **Directly learning with noisy labels. ** The field of noisy label learning has proposed several approaches to address the challenges of training models using noisy labeled data [2]. A noise transition matrix is used to capture the transition probability between the noisy labels and true labels [13, 14]. However, the size of the noise transition matrix grows exponentially as the number of dataset categories increases, making it intractable to calculate. To avoid modeling noise, some methods commonly rely on the internal noise tolerance of the classifier and aim to boost performance by regularizing undesired memorization of noisy data [15, 16, 17, 18]. Although these methods enhance the scalability of the LNL algorithm, they are highly dependent on the internal noise tolerance of the classifier and do not fully exploit the valid information in the noisy sample data [19]. Noise detection methods, on the other hand, focus on utilizing semi-supervised learning to identify and generate target probabilities. By guiding the model to the target probabilities, these methods avoid memorizing noisy labels and mitigate the effects of bad sample data [3, 20]. In addition, label correction techniques aim to generate pseudo-labels that are more consistent with real labels [21]. Some methods strengthen sample selection through unsupervised contrastive learning, thus emphasizing robust representation learning [21, 22, 23, 24]. Several studies have focused on selecting high-confidence samples and reweighting them. [PERSON]. [25] proposed a co-optimization framework that can correct labels during training by alternating updates of network parameters and labels. Similarly, [PERSON]. [9] proposed a co-teaching framework that aims to train two deep neural networks simultaneously. This framework is based on the property that deep neural networks tend to preferentially learn simple and correct samples [13]. The framework utilizes the small loss principle [4, 7, 26, 27] to filter out reliable clean samples to guide the training of the other networks. Due to the differences in the initial parameters of the two networks, they can filter out the noisy labels when guiding each other's training, thus mitigating the effect of noise on the model. Another sample-selecting method is Curriculum Learning (CL), which starts with simple samples and adds difficult samples for training as the model gradually improves [28]. Based on the CL theory, clean-labeled data are considered simple samples, while noise-labeled data are considered difficult tasks. Therefore, researchers start with confident clean samples and learn complex samples after the model is improved. Filtering clean samples can be performed by using a specific filtering loss function to rank the instances based on their characteristics [29]. **Meta-learning with noisy labels**. Meta-learning aims to learn a meta-task, which is a higher-level learning objective than classical supervised learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve label noise tasks using only a small number of training samples. For the field of LNLs, technically, meta-learning methods use part of clean datasets to guide model optimization [10, 12, 14, 30, 31, 32]. Based on meta-learning, Model-Agnostic Meta-Learning (MAML) is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, such as label noise learning [33]. A similar approach consists of finding model parameters that minimize the effect of noisy labels in meta-learning-based noise-tolerant (MLNT) training [30]. In this framework, MLNT formulates a meta-objective as training the model such that after it learns from various synthetic noisy labels using gradient update, the updated models give consistent predictions.