Results 11 to 20 of about 543,028 (283)
Self-supervised Learning: A Succinct Review
Archives of Computational Methods in Engineering, 2023 Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data.
Veenu Rani +4 more
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Self-Supervised Multimodal Learning: A Survey
IEEE Transactions on Pattern Analysis and Machine IntelligenceAccepted to IEEE T ...
Yongshuo Zong +2 more
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Longitudinal self-supervised learning [PDF]
Medical Image Analysis, 2021 Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to ...
Qingyu Zhao +3 more
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Self-Supervised Learning Across Domains [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover ...
Bucci, Silvia +5 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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Quantum self-supervised learning
Quantum Science and Technology, 2022 AbstractThe resurgence of self-supervised learning, whereby a deep learning model generates its own supervisory signal from the data, promises a scalable way to tackle the dramatically increasing size of real-world data sets without human annotation.
Jaderberg, B +5 more
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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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