Results 41 to 50 of about 8,068,470 (297)
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 ...
Yanwei Fu 0001, Leonid Sigal
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Efficiently Learning the Graph for Semi-supervised Learning
CoRR, 2023 29 pages, 9 ...
Dravyansh Sharma, Maxwell Jones
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South African Computer Journal, 2020 Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter.
Oluwafemi Oriola, Eduan KotzeĢ
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Semi-supervised Sequence Learning
CoRR, 2015 We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the
Andrew M. Dai, Quoc V. Le
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Semi-Supervised Learning with Scarce Annotations [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 Workshop on Deep Vision, CVPR ...
Rebuffi, S-A +4 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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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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Muffled Semi-Supervised Learning
CoRR, 2016 We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than ...
Akshay Balsubramani, Yoav Freund
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Semi-supervised learning integrated with classifier combination for word sense disambiguation [PDF]
, 2008 Word sense disambiguation (WSD) is the problem of determining the right sense of a polysemous word in a certain context. This paper investigates the use of unlabeled data for WSD within a framework of semi-supervised learning, in which labeled data is ...
Le, Anh-Cuong +3 more
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Towards Realistic Semi-supervised Learning
, 2022 Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation ...
Mamshad Nayeem Rizve +2 more
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