Results 11 to 20 of about 546,017 (306)
Group-based siamese self-supervised learning
Electronic Research Archive, 2023 In this paper, we introduced a novel group self-supervised learning approach designed to improve visual representation learning. This new method aimed to rectify the limitations observed in conventional self-supervised learning.
Zhongnian Li +3 more
doaj +2 more sources
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
openaire +5 more sources
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
openaire +2 more sources
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
openaire +3 more sources
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
openaire +3 more sources
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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DenseCL: A simple framework for self-supervised dense visual pre-training
Visual Informatics, 2023 Self-supervised learning aims to learn a universal feature representation without labels. To date, most existing self-supervised learning methods are designed and optimized for image classification.
Xinlong Wang +3 more
doaj +1 more source
Brain Sciences, 2021 Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning ...
Jingwei Li +6 more
doaj +1 more source
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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