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 …

Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised
models due to its effect in hurting the generalization performance of deep neural networks …

Leveraging few-shot datasets in prompt learning for Vision-Language Models eliminates the
need for manual prompt engineering while highlighting the necessity of accurate …

Multimodal sentiment analysis aims to effectively integrate information from various sources
to infer sentiment, where in many cases there are no annotations for unimodal labels …

The expanding model size and computation of deep neural networks (DNNs) have
increased the demand for efficient model deployment methods. Quantization-aware training …

A prominent effect of label noise on neural networks is the disruption of the consistency of
predictions. While prior efforts primarily focused on predictions' consistency at the individual …

Learning with noisy labels has been studied to address incorrect label annotations in real-
world applications. In this paper, we present ChiMera, a two-stage learning-from-noisy …

Label noise poses an important challenge in machine learning, especially in deep learning,
in which large models with high expressive power dominate the field. Models of that kind are …

Learning from corrupted data marginally degrades model performance. As deep learning
proliferates, the need for large, accurately labeled datasets becomes crucial. Central to this …

The collection of large-scale datasets inevitably introduces noisy labels, leading to a
substantial degradation in the performance of deep neural networks (DNNs). Although …