3 accepted papers
Training with instance-dependent label noise poses a critical challenge in deep learning, severely impeding model generalization. Existing methods often struggle to break the vicious cycle where corrupted labels degrade feature representations, which in turn impairs noise identification. To overcome
In recent years, RF fingerprinting (RFF) has emerged as a promising technology for wireless device authentication. However, temporal variations in device load and temperature, along with channel effects, lead to inconsistencies in RFF distributions between training and testing phases. As a result,
The recent flourish of deep learning on various tasks is largely accredited to the rich and high-quality labeled data. Nonetheless, collecting sufficient labeled samples is not very practical for many real applications. Few-shot Learning (FSL) provides a promising solution that allows a model to lea…