Results 41 to 50 of about 546,017 (306)
Self-supervised learning methods and applications in medical imaging analysis: a survey [PDF]
PeerJ Computer Science, 2022 The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement.
Saeed Shurrab, Rehab Duwairi
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Multi-task Self-Supervised Visual Learning [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 Published at ICCV ...
Doersch, C, Zisserman, A
openaire +3 more sources
Self‐supervised learning with randomised layers for remote sensing
Electronics Letters, 2021 This letter presents a new self‐supervised learning approach based on randomised layers for remote sensing. Our method is basically based on the Tile2Vec approach, which is one of the state‐of‐the‐art self‐supervised learning approaches for remote ...
Heechul Jung, Taegyun Jeon
doaj +1 more source
Self-Supervised Learning for Solar Radio Spectrum Classification
Universe, 2022 Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent solar
Siqi Li +4 more
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Self-Supervised Deep Visual Odometry with Online Adaptation
, 2020 Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different ...
Cao, Yingdian +5 more
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Experimental Case Study of Self-Supervised Learning for Voice Spoofing Detection
IEEE Access, 2023 This study aims to improve the performance of voice spoofing attack detection through self-supervised pre-training. Supervised learning needs appropriate input variables and corresponding labels for constructing the machine learning models that are to be
Yerin Lee +3 more
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Improving drug–target affinity prediction by adaptive self-supervised learning [PDF]
PeerJ Computer ScienceComputational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction.
Qing Ye, Yaxin Sun
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Journal of King Saud University: Computer and Information Sciences, 2023 Vehicle routing optimization is a crucial responsibility of transportation service providers, which can significantly reduce operating expenses and improve client satisfaction.
Qi Wang, Chengwei Zhang, Chunlei Tang
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CASSL: Curriculum Accelerated Self-Supervised Learning
, 2018 Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces.
Gandhi, Dhiraj +3 more
core +1 more source
FACETSSelf-supervised learning enables the creation of algorithms that outperform supervised pre-training methods in numerous computer vision tasks. This paper provides a comprehensive overview of self-supervised learning applications across various X-ray ...
Ivan Martinović +6 more
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