Results 1 to 10 of about 543,028 (283)
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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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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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
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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
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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
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