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Semi-Supervised Learning with Scarce Annotations [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small.
Ehrhardt, Sebastien +4 more
core +4 more sources
A Discussion of Semi-Supervised Learning and Transduction [PDF]
, 2006 A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research.
Chapelle Olivier +2 more
openalex +3 more sources
A review of semi-supervised learning for text classification. [PDF]
Artif Intell Rev, 2023 A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or ...
Duarte JM, Berton L.
europepmc +2 more sources
A survey on semi-supervised learning [PDF]
Machine Learning, 2019 Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks.
Jesper E. van Engelen, H. Hoos
semanticscholar +2 more sources
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [PDF]
International Conference on Learning Representations, 2023 The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
Hao Chen +8 more
semanticscholar +1 more source
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning.
Changhan Wang +8 more
semanticscholar +1 more source
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
doaj +1 more source
Contrastive Semi-Supervised Learning for Underwater Image Restoration via Reliable Bank [PDF]
Computer Vision and Pattern Recognition, 2023 Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress.
Shirui Huang +4 more
semanticscholar +1 more source
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning [PDF]
International Conference on Learning Representations, 2022 Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Yidong Wang +8 more
semanticscholar +1 more source