Zobrazeno 1 - 10
of 482
pro vyhledávání: '"noisy label"'
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 8011-8026 (2024)
Abstract The success of current deep learning models depends on a large number of precise labels. However, in the field of medical image segmentation, acquiring precise labels is labor-intensive and time-consuming. Hence, the challenge of achieving a
Externí odkaz:
https://doaj.org/article/92172de3b90546eab9dcf5d1b654c00d
Publikováno v:
Visual Computing for Industry, Biomedicine, and Art, Vol 7, Iss 1, Pp 1-16 (2024)
Abstract Learning with noisy labels aims to train neural networks with noisy labels. Current models handle instance-independent label noise (IIN) well; however, they fall short with real-world noise. In medical image classification, atypical samples
Externí odkaz:
https://doaj.org/article/74fbbe77223448658ef639a8e1b9e375
Publikováno v:
Frontiers in Genetics, Vol 15 (2024)
Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteris
Externí odkaz:
https://doaj.org/article/d151a02571fb4a418fdab822ccead36b
Autor:
Praveen Kumar, Christophe G. Lambert
Publikováno v:
PeerJ Computer Science, Vol 10, p e2451 (2024)
Positive and unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). P
Externí odkaz:
https://doaj.org/article/721f96e05e9a471e99476370559e4c57
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4157-4174 (2024)
Abstract Machine learning (ML) is an approach driven by data, and as research in machine learning progresses, the issue of noisy labels has garnered widespread attention. Noisy labels can significantly reduce the accuracy of supervised classification
Externí odkaz:
https://doaj.org/article/a98d7d79902e4a218b873415f26ee62c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 1839-1856 (2024)
High-resolution land cover mapping (LCM) is pivotal in numerous disciplines but still challenging to be acquired because traditional supervised methods require a substantial number of high-resolution labels that is labouring and expensive. To this is
Externí odkaz:
https://doaj.org/article/6a9d7428d848438c8d2837c3ec077ae2
Autor:
Hongbo Zhu, Tao Yu, Xiaofei Mi, Jian Yang, Chuanzhao Tian, Peizhuo Liu, Jian Yan, Yuke Meng, Zhenzhao Jiang, Zhigao Ma
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2443 (2024)
Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A
Externí odkaz:
https://doaj.org/article/b35891f415f041d5a9b1b848b943c2ef
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2499 (2024)
Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a univ
Externí odkaz:
https://doaj.org/article/9832991a6f9b4418bedefcbd162a83b1
Akademický článek
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Publikováno v:
IEEE Access, Vol 11, Pp 140496-140505 (2023)
Facial expression recognition (FER) has been extensively studied in various applications over the past few years. However, in real facial expression datasets, labels can become noisy due to the ambiguity of expressions, the similarity between classes
Externí odkaz:
https://doaj.org/article/481e228e61434c6789aed54ca23a18a6