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Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an...
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Jie Wang, Jie Yang, Jiafan He and Dongliang Peng
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier us...
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Yugen Yi, Haoming Zhang, Ningyi Zhang, Wei Zhou, Xiaomei Huang, Gengsheng Xie and Caixia Zheng
As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature se...
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Adam Olesinski and Zbigniew Piotrowski
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Dongming Wang, Li Xu, Wei Gao, Hongwei Xia, Ning Guo and Xiaohan Ren
As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification ...
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Kenneth David Strang
A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project us...
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Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi
Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processin...
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Rui Zhang, Mingwei Yao, Zijie Qiu, Lizhuo Zhang, Wei Li and Yue Shen
Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data colle...
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Yang Zhang, Yuan Feng, Shiqi Wang, Zhicheng Tang, Zhenduo Zhai, Reid Viegut, Lisa Webb, Andrew Raedeke and Yi Shang
Waterfowl populations monitoring is essential for wetland conservation. Lately, deep learning techniques have shown promising advancements in detecting waterfowl in aerial images. In this paper, we present performance evaluation of several popular superv...
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MohammadHossein Reshadi, Wen Li, Wenjie Xu, Precious Omashor, Albert Dinh, Scott Dick, Yuntong She and Michael Lipsett
Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially ...
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