Suchergebnisse - Learning with Noisy Labels in Machine Learning

Probabilistic machine learning for noisy labels in Earth observation von Kondylatos, Spyros, Bountos, Nikolaos Ioannis, Prapas, Ioannis, Zavras, Angelos, Camps-Valls, Gustau, Papoutsis, Ioannis

“… Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models …”
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Agreeing to disagree: active learning with noisy labels without crowdsourcing von Bouguelia, Mohamed-Rafik, Nowaczyk, Slawomir, Santosh, K. C., Verikas, Antanas

“… We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing …”
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Towards harnessing feature embedding for robust learning with noisy labels von Zhang, Chuang, Shen, Li, Yang, Jian, Gong, Chen

“… To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels …”
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Curriculum-Based Federated Learning for Machine Fault Diagnosis With Noisy Labels von Sun, Wenjun, Yan, Ruqiang, Jin, Ruibing, Zhao, Rui, Chen, Zhenghua

“… Federated learning (FL) has emerged as an effective machine-learning paradigm for collaborative machine fault diagnosis in a privacy-preserving scheme …”
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Probabilistic instance dependent label refinement for noisy label learning von He, Hao-Yuan, Liu, Yu, Liu, Ren-Biao, Xie, Zheng, Li, Ming

“… By adjusting the combination coefficient of the noisy label, the impact of noise is reduced, which in turn makes the training process more robust …”
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Partial Multi-Label Learning With Noisy Label Identification von Xie, Ming-Kun, Huang, Sheng-Jun

“… Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels …”
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A confident learning-based support vector machine for robust ground classification in noisy label environments von Zhang, Xin-Yue, Zhang, Xiao-Ping, Yu, Hong-Gan, Liu, Quan-Sheng

“… Geological labels obtained from field exploration have potential errors due to technique limitations and subjective interference, leading to noisy labels when developing ground-machine interaction …”
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A General Model for Noisy Labels in Machine Learning von Dawson, Glenn

“… Machine learning is an ever-growing and increasingly pervasive presence in everyday life …”
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CPL: consistent prompt learning for noisy label facial expression recognition von Zhang, Jinglin, Wang, Qiangchang, Li, Jing, Li, Zhaohui, Zhang, Xinxin, Zhao, Peng, Yin, Yilong, Lu, Huimin

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Adaptive Hierarchical Similarity Metric Learning with Noisy Labels von Yan, Jiexi, Luo, Lei, Deng, Cheng, Huang, Heng

“… However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data …”
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No regret sample selection with noisy labels von Song, Heon, Mitsuo, Nariaki, Uchida, Seiichi, Suehiro, Daiki

“… Deep neural networks (DNNs) suffer from noisy-labeled data because of the risk of overfitting …”
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A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels von Wu, Songhua, Zhou, Tianyi, Du, Yuxuan, Yu, Jun, Han, Bo, Liu, Tongliang

“… Many machine learning algorithms are known to be fragile on simple instance-independent noisy labels …”
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Learning from Weak and Noisy Labels for Semantic Segmentation von Lu, Zhiwu, Fu, Zhenyong, Xiang, Tao, Han, Peng, Wang, Liwei, Gao, Xin

“… , these `free' tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels …”
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A class sensitivity feature guided T-type generative model for noisy label classification von Bai, Yidi, Cui, Hengjian

“… Large-scale datasets inevitably contain noisy labels, which induces weak performance of deep neural networks (DNNs …”
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Searching to Exploit Memorization Effect in Deep Learning With Noisy Labels von Yang, Hansi, Yao, Quanming, Han, Bo, Kwok, James T.

“… Sample selection approaches are popular in robust learning from noisy labels. However, how to control the selection process properly so that deep networks can benefit from the memorization effect is a hard problem …”
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Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels von Wang, Yikai, Fu, Yanwei, Sun, Xinwei

“… In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels …”
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Regularly Truncated M-Estimators for Learning With Noisy Labels von Xia, Xiaobo, Lu, Pengqian, Gong, Chen, Han, Bo, Yu, Jun, Yu, Jun, Liu, Tongliang

“… 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 procedure …”
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The fuzzy support vector data description based on tightness for noisy label detection von Wu, Xiaoying, Liu, Sanyang, Bai, Yiguang

“… 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 …”
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Adaptive Learning for Dynamic Features and Noisy Labels von Gu, Shilin, Xu, Chao, Hu, Dewen, Hou, Chenping

“… Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited …”
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IRNet: Iterative Refinement Network for Noisy Partial Label Learning von Lian, Zheng, Xu, Mingyu, Chen, Lan, Sun, Licai, Liu, Bin, Feng, Lei, Tao, Jianhua

“… Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels …”
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