Results 1 to 10 of about 219,224 (269)
Supervised learning and Co-training [PDF]
Theoretical Computer Science, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Malte Darnstädt +2 more
openaire +3 more sources
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
doaj +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
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
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
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
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
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
Supervised Dictionary Learning
CoRR, 2008 It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones.
Mairal, Julien +4 more
openaire +4 more sources
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