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Fan Huang , Nan Yang, Huaming Chen , Wei Bao and Dong Yuan
With the widespread use of end devices, online multi-label learning has become popular as the data generated by users using the Internet of Things devices have become huge and rapidly updated. However, in many scenarios, the user data are often generated...
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Youngki Park and Youhyun Shin
In this paper, we introduce an efficient approach to multi-label image classification that is particularly suited for scenarios requiring rapid adaptation to new classes with minimal training data. Unlike conventional methods that rely solely on neural n...
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Gergely Márk Csányi, Renátó Vági, Andrea Megyeri, Anna Fülöp , Dániel Nagy, János Pál Vadász and István Üveges
Few-shot learning is a deep learning subfield that is the focus of research nowadays. This paper addresses the research question of whether a triplet-trained Siamese network, initially designed for multi-class classification, can effectively handle multi...
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Dezheng Zhang, Peng Li and Aziguli Wulamu
Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning methods...
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Zhuohao Zhou, Chunyue Lu, Wenchao Wang, Wenhao Dang and Ke Gong
The training of deep neural networks usually requires a lot of high-quality data with good annotations to obtain good performance. However, in clinical medicine, obtaining high-quality marker data is laborious and expensive because it requires the profes...
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Sergey A. Soldatov, Danil M. Pashkov, Sergey A. Guda, Nikolay S. Karnaukhov, Alexander A. Guda and Alexander V. Soldatov
Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each yea...
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He Yin, Keming Mao, Jianzhe Zhao, Huidong Chang, Dazhi E and Zhenhua Tan
This study considered heated metal mark attribute recognition based on compressed convolutional neural networks (CNNs) models. Based on our previous works, the heated metal mark image benchmark dataset was further expanded. State-of-the-art lightweight C...
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Wenjun Bai, Changqin Quan and Zhiwei Luo
Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training...
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