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Yusuf Brima, Ulf Krumnack, Simone Pika and Gunther Heidemann
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are ...
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Yubo Zheng, Yingying Luo, Hengyi Shao, Lin Zhang and Lei Li
Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data. However, the application of data augmentation methods duri...
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Alexander Chowdhury, Jacob Rosenthal, Jonathan Waring and Renato Umeton
Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine...
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Padraig Corcoran and Irena Spasic
Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine lear...
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Yunfan Gao, Yun Xiong, Siqi Wang and Haofen Wang
Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activ...
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Shenghan Zhou, Tianhuai Wang, Linchao Yang, Zhao He and Siting Cao
This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as...
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Achintya Kumar Sarkar and Zheng-Hua Tan
Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extracting bottleneck (BN) features, the key considerations include training targ...
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Esmaeil Zahedi, Mohamad Saraee, Fatemeh Sadat Masoumi and Mohsen Yazdinejad
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and C...
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Young-Joo Han and Ha-Jin Yu
Deep learning-based denoising methods have proved efficient for medical imaging. Obtaining a three-dimensional representation of a scanned object is essential, such as in the computed tomography (CT) system. A sufficient radiation dose needs to be irradi...
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