Results 31 to 40 of about 341,780 (320)
Semi-Supervised Domain Adaptive Structure Learning
IEEE Transactions on Image Processing, 2022 Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of ...
Can Qin +5 more
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Benchmarking the Semi-Supervised Naïve Bayes Classifier [PDF]
, 2015 Semi-supervised learning involves constructing predictive models with both labelled and unlabelled training data. The need for semi-supervised learning is driven by the fact that unlabelled data are often easy and cheap to obtain, whereas labelling data ...
Bagnall, Anthony +2 more
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Semi‐supervised learning dehazing algorithm based on the OSV model
IET Image Processing, 2023 Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging.
Lijun Zhu +5 more
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An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-Supervised Methods
IEEE Access, 2022 Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-
Karliane Medeiros Ovidio Vale +3 more
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AlgorithmsPredicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the
Sami Kabir +2 more
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Dual Learning-Based Safe Semi-Supervised Learning
IEEE Access, 2018 In many real-world applications, labeled instances are generally limited and expensively collected, while the most instances are unlabeled and the amount is often sufficient.
Haitao Gan +3 more
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Adversarial Dropout for Supervised and Semi-supervised Learning
, 2017 Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs
Moon, Il-Chul +3 more
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Semi-supervised Vocabulary-Informed Learning [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with ...
Fu, Yanwei, Sigal, Leonid
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Full body virtual try‐on with semi‐self‐supervised learning
Electronics Letters, 2021 This paper proposes a full body virtual try‐on which handles both top and bottom garments and generates realistic try‐on images. For the full body virtual try‐on, this paper addresses lack of suitable training data to align and fit top and bottom ...
Hyug‐Jae Lee +5 more
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Semi-Supervised Deep Learning for Fully Convolutional Networks
, 2017 Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training ...
D García-Lorenzo +4 more
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