Results 41 to 50 of about 315,285 (319)
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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Monocular obstacle avoidance with persistent Self-Supervised Learning [PDF]
, 2014 Raw data belonging to paper: Persistent self-supervised learning: from stereo to monocular vision for obstacle ...
van Hecke, K.G. (Kevin)
core +1 more source
Predicting rank for scientific research papers using supervised learning
Applied Computing and Informatics, 2019 Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task ...
Mohamed El Mohadab +2 more
doaj +1 more source
Information, 2021 Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by ...
Ruobin Qi +3 more
doaj +1 more source
Supervising Unsupervised Learning
CoRR, 2017 We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms.
Vikas K. Garg 0001, Adam Kalai
openaire +2 more sources
Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry
Sensors, 2021 Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes.
Youwei Li +4 more
doaj +1 more source
Augmenting Few-Shot Learning With Supervised Contrastive Learning
IEEE Access, 2021 Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios.
Taemin Lee, Sungjoo Yoo
doaj +1 more source
Gated Self-supervised Learning for Improving Supervised Learning
2024 IEEE Conference on Artificial Intelligence (CAI)In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common. However, relying solely on rotation as a self-supervised transformation can limit the ability of the model to learn rich features from the data.
Erland Hilman Fuadi +3 more
openaire +2 more sources
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
doaj +1 more source
Early Impact of Childhood Opportunity on Neurocognitive Outcomes in Sickle Cell Disease
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction Neurocognitive impairment is a well‐recognized complication of sickle cell disease (SCD) that begins early in childhood and persists across development. While cerebrovascular injury contributes substantially to risk, neurocognitive deficits are also observed in children without overt or silent cerebral infarctions, suggesting ...
Julia E. LaMotte +5 more
wiley +1 more source