# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 1 Introduction  
Deep learning relies on extensive datasets and has garnered significant achievements in domains like image classification [1], target detection [2], and image segmentation [3]. However, in real-world applications, annotating data can be prohibitively expensive. The quality of annotated samples might be compromised by subjective biases in manual labeling or tool limitations, resulting in label noise. The performance of DNNs in generalization frequently suffers when trained on datasets containing noisy labels. In real-world situations, training data are commonly gathered from various origins such as web searches [4] and crowdsourcing [5] in biased datasets. Consequently, effectively training DNNs with such noisy datasets poses a significant challenge within the realm of deep learning. To tackle this challenge, numerous strategies have been suggested [6], falling into four primary categories. The initial strategy revolves around robust loss functions. While conventional loss functions such as cross-entropy (CE) [7] and mean squared error (MSE) [8] are sturdy, they may falter when dealing with intricate datasets. Hence, robust loss functions are formulated, incorporating supplementary steps and algorithms to enhance performance. The second strategy revolves around importance weighting, where smaller weights are allocated to potentially noisy samples while cleaner samples receive larger weights. This diminishes the impact of noisy labels on classification accuracy without entirely disregarding them. Moving to the third approach, loss correction operates under the assumption that noisy labels represent corrupted versions of clean labels, attributable to an unidentified noise transfer matrix. The objective of loss correction is to accurately learn this matrix to rectify the labels. Lastly, the fourth approach termed the pseudo-label method, is dedicated torectifying identified dubious noisy labels to their accurate values. Unlike merely assigning higher weights to clean samples, the pseudo-label method delves into adjusting potentially incorrect labels. Typical pseudo-label techniques guide the training procedure toward the correct class by estimating the probability of rectifying the noise transfer matrix [9; 10]. Alternatively, another approach entails employing the network to predict pseudo-labels for directly generated noisy labels, e.g., the approach proposed by [PERSON] et al. [11] involves assigning a pseudo-label within the bootstrap loss, which constitutes a convex amalgamation of the original label and the predicted label, subsequently integrating it into the training regimen. Although these techniques can produce pseudo-labels for noisy labels, the accuracy of such pseudo-labels might suffer if the classifier is inadequately trained. Additionally, these methods often overlook the impact of feature noise on classification performance. To tackle the aforementioned issues, this paper presents a method for generating feature labels from the feature layer to rectify the noisy labels. More precisely, we cluster the features extracted from the network, find the clustering center for each class, calculate its cosine similarity with each sample feature, and subsequently assign the feature labels based on the magnitude of the cosine similarity. Finally, we utilize a combination of the exponential moving average of pseudo-labels from the previous iteration, predicted labels, and feature labels to refine the accuracy of the pseudo-labels. The principal contributions of this study can be summarized as follows. * We introduce a simplified pseudo-labeling method that amalgamates the exponential moving average derived from the most recent round of pseudo-labels, predicted labels, and feature labels to form refined pseudo-labels. This approach is adept at handling datasets with differing noise types and levels. * We conducted thorough experiments on four benchmark datasets and one real dataset, covering four diverse noise types and two distinct noise levels.