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Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [PDF]
Jisuanji kexue, 2022 Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of ...
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
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Enhancing rare cardiac disease classification with GAN-augmented supervised and self-supervised learning: a case study on Brugada syndrome [PDF]
Eur Heart J Digit HealthB Zanchi, G Monachino, G Conte, F Faraci
europepmc +2 more sources
Longitudinal self-supervised learning [PDF]
Medical Image Analysis, 2021 Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to ...
Qingyu Zhao +3 more
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Self-Supervised Transfer Learning from Natural Images for Sound Classification
Applied Sciences, 2021 We propose the implementation of transfer learning from natural images to audio-based images using self-supervised learning schemes. Through self-supervised learning, convolutional neural networks (CNNs) can learn the general representation of natural ...
Sungho Shin +4 more
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Europhysics Letters, 2023 Abstract In neural network's literature, Hebbian learning traditionally refers to the procedure by which the Hopfield model and its generalizations store archetypes (i.e., definite patterns that are experienced just once to form the synaptic matrix).
Francesco Alemanno +4 more
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IEEE Access, 2023 Despite the remarkable progress of self-supervised learning (SSL), how self-supervised representations generalize to out-of-distribution data remains little understood.
Samira Zare, Hien Van Nguyen
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Semi-supervised learning [PDF]
Machine Learning, 1989 The distribution-independent model of (supervised) concept learning due to Valiant (1984) is extended to that of semi-supervised learning (ss-learning), in which a collection of disjoint concepts is to be simultaneously learned with only partial information concerning concept membership available to the learning algorithm.
Raymond Board, Leonard Pitt
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CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self ...
L. Scheibenreif, M. Mommert, D. Borth
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Brain Sciences, 2021 Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning ...
Jingwei Li +6 more
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Weakly supervised machine learning
CAAI Transactions on Intelligence Technology, 2023 Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground‐truth value.
Zeyu Ren, Shuihua Wang, Yudong Zhang
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