Classification models categorize objects into given classes, guided by training samples with
input features and labels. In practice, however, labels can be corrupted by human error or …

Several factors are associated with the success of deep learning. One of the most important
reasons is the availability of large-scale datasets with clean annotations. However, obtaining …

Robust loss minimization is an important strategy for handling robust learning issue on noisy
labels. Current approaches for designing robust losses involve the introduction of noise …

Large-scale image datasets frequently contain unavoidable noisy labels, resulting in
overfitting in deep neural networks and declining performance. Most existing methods for …

Learning from noisy data has become essential for adapting deep learning models to real-
world applications. Traditional methods often involve first evaluating the noise and then …

Large language models (LLMs) have revolutionised numerous fields, with LLM-as-a-service
(LLMSaaS) having a strong generalisation ability that offers accessible solutions directly …

Deep neural networks (DNNs) have achieved breakthrough progress in various fields,
largely owing to the support of large-scale datasets with manually annotated labels …

Deep neural networks and large-scale datasets have revolutionized the field of machine
learning. However, these large networks are susceptible to overfitting to label noise …

Contrary to centralized federated learning (CFL), decentralized federated learning (DFL)
allows clients to cooperate in training their local models without relying on a central …