Enhancing rare cardiac disease classification with GAN-augmented supervised and self-supervised learning: a case study on Brugada syndrome [PDF]
Eur Heart J Digit HealthB Zanchi, G Monachino, G Conte, F Faraci
europepmc +2 more sources
Temporal and spatial self supervised learning methods for electrocardiograms [PDF]
Scientific ReportsThe limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based ...
Wenping Chen +3 more
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CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self ...
L. Scheibenreif, M. Mommert, D. Borth
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EVALUATION OF SELF-SUPERVISED LEARNING APPROACHES FOR SEMANTIC SEGMENTATION OF INDUSTRIAL BURNER FLAMES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 In recent years, self-supervised learning has made tremendous progress in closing the gap to supervised learning due to the rapid development of more sophisticated approaches like SimCLR, MoCo, and SwAV.
S. Landgraf +6 more
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Self supervised contrastive learning for digital histopathology
Machine Learning with Applications, 2022 Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity of labeled datasets.
Ozan Ciga, Tony Xu, Anne Louise Martel
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Self-Supervised Transfer Learning from Natural Images for Sound Classification
Applied Sciences, 2021 We propose the implementation of transfer learning from natural images to audio-based images using self-supervised learning schemes. Through self-supervised learning, convolutional neural networks (CNNs) can learn the general representation of natural ...
Sungho Shin +4 more
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IEEE Access, 2023 Despite the remarkable progress of self-supervised learning (SSL), how self-supervised representations generalize to out-of-distribution data remains little understood.
Samira Zare, Hien Van Nguyen
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Siamese-Network Based Signature Verification using Self Supervised Learning
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes.
Muhammad Fawwaz Mayda, Aina Musdholifah
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A self-supervised deep learning method for data-efficient training in genomics
Communications Biology, 2023 Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine ...
Hüseyin Anil Gündüz +7 more
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Self-Supervised Representation Learning for Document Image Classification
IEEE Access, 2021 Supervised learning, despite being extremely effective, relies on expensive, time-consuming, and error-prone annotations. Self-supervised learning has recently emerged as a strong alternate to supervised learning in a range of different domains as ...
Shoaib Ahmed Siddiqui +2 more
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