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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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Xuefeng Zhang, Youngsung Kim, Young-Chul Chung, Sangcheol Yoon, Sang-Yong Rhee and Yong Soo Kim
Large-scale datasets, which have sufficient and identical quantities of data in each class, are the main factor in the success of deep-learning-based classification models for vision tasks. A shortage of sufficient data and interclass imbalanced data dis...
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Zhuo Wang, Haojie Chen, Hongde Qin and Qin Chen
In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color dis...
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Samuel-Soma M. Ajibade, Festus Victor Bekun, Festus Fatai Adedoyin, Bright Akwasi Gyamfi and Anthonia Oluwatosin Adediran
This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2...
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Xiaoxi Zhang, Yuan Gao, Xin Wang, Jun Feng and Yan Shi
Inferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, s...
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Junwen Lu, Jinhui Wang, Xiaojun Wei, Keshou Wu and Guanfeng Liu
There is relatively little research on deep learning for anomaly detection within the field of deep learning. Existing deep anomaly detection methods focus on the learning of feature reconstruction, but such methods mainly learn new feature representatio...
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Ali Reza Sajun and Imran Zualkernan
Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SS...
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Ali Reza Sajun, Imran Zualkernan and Donthi Sankalpa
Semi-supervised learning can effectively be used to detect the chest X-rays affected by COVID-19.
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Muhammad Mostafa Monowar, Md. Abdul Hamid, Faris A. Kateb, Abu Quwsar Ohi and M. F. Mridha
Plant diseases have been one of the most threatening scenarios to farmers. Although most plant diseases can be identified by observing leaves, it often requires human expertise. The recent improvements in computer vision have led to introduce disease cla...
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Edvaldo Domingos, Blessing Ojeme and Olawande Daramola
Until recently, traditional machine learning techniques (TMLTs) such as multilayer perceptrons (MLPs) and support vector machines (SVMs) have been used successfully for churn prediction, but with significant efforts expended on the configuration of the t...
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Theiab Alzahrani, Baidaa Al-Bander and Waleed Al-Nuaimy
Makeup can disguise facial features, which results in degradation in the performance of many facial-related analysis systems, including face recognition, facial landmark characterisation, aesthetic quantification and automated age estimation methods. Thu...
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Ali Al-Laith, Muhammad Shahbaz, Hind F. Alaskar and Asim Rehmat
At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furth...
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Jiantao Liu, Quanlong Feng, Ying Wang, Bayartungalag Batsaikhan, Jianhua Gong, Yi Li, Chunting Liu and Yin Ma
With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust polluti...
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Hong Wang, Qingsong Xu and Lifeng Zhou
To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML) form) or not. Previous studies have focused on supervised classification with labeled examples. However, ...
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Adil Redaoui, Amina Belalia and Kamel Belloulata
Deep network-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structura...
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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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Darian M. Onchis, Flavia Costi, Codruta Istin, Ciprian Cosmin Secasan and Gabriel V. Cozma
(1) Background: Lung cancers are the most common cancers worldwide, and prostate cancers are among the second in terms of the frequency of cancers diagnosed in men. Automatic ranking of the risk groups of such diseases is highly in demand, but the clinic...
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Adam Olesinski and Zbigniew Piotrowski
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Hyunmin Gwak, Yongho Jeong, Chanyeong Kim, Yonghak Lee, Seongmin Yang and Sunghwan Kim
The key to semi-supervised semantic segmentation is to assign the appropriate pseudo-label to the pixels of unlabeled images. Recently, various approaches to consistency-based training and the filtering of reliable pseudo-labels have shown remarkable res...
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Shangchen Ma and Chunlin Song
Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve good...
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