Results 11 to 20 of about 549,786 (309)
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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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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Audio self-supervised learning: A survey
Patterns, 2022 Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
Shuo Liu +7 more
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Structure-aware protein self-supervised learning
Bioinformatics, 2023 Abstract Motivation Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major ...
Can (Sam) Chen +4 more
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Self‐supervised learning for outlier detection [PDF]
Stat, 2021 The identification of outliers is mainly based on unannotated data and therefore constitutes an unsupervised problem. The lack of a label leads to numerous challenges that do not occur or only occur to a lesser extent when using annotated data and supervised methods.
Jan Diers, Christian Pigorsch
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Self-Supervised Dialogue Learning [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 11pages, 2 figures, accepted to ACL ...
Wu, Jiawei +2 more
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Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for self-supervised learning that represents unknown labels as ...
Lang, Hunter, Poon, Hoifung
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Mixup Feature: A Pretext Task Self-Supervised Learning Method for Enhanced Visual Feature Learning
IEEE Access, 2023 Self-supervised learning has emerged as an increasingly popular research topic within the field of computer vision. In this study, we propose a novel self-supervised learning approach based on Mixup features as pretext tasks.
Jiashu Xu, Sergii Stirenko
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Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks
Sensors, 2023 The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant ...
Josue Genaro Almaraz-Rivera +2 more
doaj +1 more source