Results 21 to 30 of about 315,285 (319)
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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Semi-supervised Learning for Anomalous Trajectory Detection [PDF]
, 2008 A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a ...
Fisher, Bob +3 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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Building One-Shot Semi-Supervised (BOSS) Learning Up to Fully Supervised Performance
Frontiers in Artificial Intelligence, 2022 Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications.
Leslie N. Smith, Adam Conovaloff
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Learning to Learn from Weak Supervision by Full Supervision
CoRR, 2017 Accepted at NIPS Workshop on Meta-Learning (MetaLearn 2017), Long Beach, CA ...
Dehghani, M. +3 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
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Review of Self-supervised Learning Methods in Field of ECG [PDF]
Jisuanji kexue yu tansuoDeep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically ...
HAN Han, HUANG Xunhua, CHANG Huihui, FAN Haoyi, CHEN Peng, CHEN Jijia
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Geostatistical semi-supervised learning for spatial prediction
Artificial Intelligence in Geosciences, 2022 Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
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Adversarial Dropout for Supervised and Semi-Supervised Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Recently, training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has improved the generalization performance of neural networks. In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of ...
Sungrae Park +3 more
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Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context [PDF]
, 2022 Background and objectives: The development of mobile and on the edge appli-cations that embed deep convolutional neural models has the potential to revolutionisebiomedicine.
Díaz-Pinto, Andrés +5 more
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