Provable dynamic fusion for low-quality multimodal data
The inherent challenge of multimodal fusion is to precisely capture the cross-modal
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …
Regularly truncated m-estimators for learning with noisy labels
The sample selection approach is very popular in learning with noisy labels. As deep
networks “learn pattern first”, prior methods built on sample selection share a similar training …
networks “learn pattern first”, prior methods built on sample selection share a similar training …
Noisy pair corrector for dense retrieval
Most dense retrieval models contain an implicit assumption: the training query-document
pairs are exactly matched. Since it is expensive to annotate the corpus manually, training …
pairs are exactly matched. Since it is expensive to annotate the corpus manually, training …
Unsupervised domain-agnostic fake news detection using multi-modal weak signals
The emergence of social media as one of the main platforms for people to access news has
enabled the wide dissemination of fake news, having serious impacts on society. Thus, it is …
enabled the wide dissemination of fake news, having serious impacts on society. Thus, it is …
Uncertainty-aware non-invasive patient–ventilator asynchrony detection using latent Gaussian mixture generative classifier with noisy label correction
Patient–ventilator asynchrony (PVA) refers to instances where a mechanical ventilator's
cycles are desynchronised from the patient's breathing efforts, and may result in patient …
cycles are desynchronised from the patient's breathing efforts, and may result in patient …
Noisy Label Processing for Classification: A Survey
M Li, C Zhu - arXiv preprint arXiv:2404.04159, 2024 - arxiv.org
In recent years, deep neural networks (DNNs) have gained remarkable achievement in
computer vision tasks, and the success of DNNs often depends greatly on the richness of …
computer vision tasks, and the success of DNNs often depends greatly on the richness of …
Learning Confident Classifiers in the Presence of Label Noise
AA Hashmi, A Zhumabayeva, N Kotelevskii… - arXiv preprint arXiv …, 2023 - arxiv.org
The success of Deep Neural Network (DNN) models significantly depends on the quality of
provided annotations. In medical image segmentation, for example, having multiple expert …
provided annotations. In medical image segmentation, for example, having multiple expert …
Benchmarking machine learning methods for the identification of mislabeled data
Supervised machine learning recently gained growing importance in various fields of
research. To train reliable models, data scientists need credible data, which is not always …
research. To train reliable models, data scientists need credible data, which is not always …