Results 21 to 30 of about 219,224 (269)
Learning to Learn in a Semi-supervised Fashion [PDF]
, 2020 To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval.
Yun-Chun Chen +2 more
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A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation
EAI Endorsed Transactions on Internet of Things, 2022 Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep
Wasswa Shafik +3 more
doaj +1 more source
Physics-constrained indirect supervised learning
Theoretical and Applied Mechanics Letters, 2020 : This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-constrained loss based on the physical mechanism to train the model.
Yuntian Chen, Dongxiao Zhang
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Tailoring Self-Supervision for Supervised Learning
, 2022 Accepted to ECCV 2022.
WonJun Moon, Ji-Hwan Kim, Jae-Pil Heo
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Sensors, 2022 The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield.
Shrinidhi Adke +3 more
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Semi-supervised Learning Algorithm Based on Maximum Margin and Manifold Hypothesis [PDF]
Jisuanji kexueSemi-supervised learning is a weakly supervised learning pattern between supervised learning and unsupervised lear-ning.It combines a small number of labeled instances with a large number of unlabeled instances to build a model during the process of ...
DAI Wei, CHAI Jing, LIU Yajiao
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An Improved Algorithm of Drift Compensation for Olfactory Sensors
Applied Sciences, 2022 This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm.
Siyu Lu +6 more
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AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden
Sensors, 2023 The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their
Oliver J. Fisher +4 more
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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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Supervised Contrastive Learning
CoRR, 2020 Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or significantly outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss.
Prannay Khosla +8 more
openaire +3 more sources