Weakly supervised machine learning [PDF]
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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Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery [PDF]
Sensors, 2022 The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield.
Shrinidhi Adke +3 more
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MetaFL: Metamorphic fault localisation using weakly supervised deep learning [PDF]
IET Software, 2023 Deep‐Learning‐based Fault Localisation (DLFL) leverages deep neural networks to learn the relationship between statement behaviour and program failures, showing promising results.
Lingfeng Fu +5 more
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A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning [PDF]
SensorsThe annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image ...
Shaolong Chen, Zhiyong Zhang
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Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning [PDF]
Scientific Reports, 2021 In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or ...
Atsushi Teramoto +7 more
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SentiUrdu-1M: A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learning [PDF]
PLoS ONE, 2023 Low-resource languages are gaining much-needed attention with the advent of deep learning models and pre-trained word embedding. Though spoken by more than 230 million people worldwide, Urdu is one such low-resource language that has recently gained ...
Abdul Ghafoor +5 more
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Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
Applied Sciences, 2023 Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain.
Yating Kong +3 more
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OVERVIEW OF COMPUTER VISION SUPERVISED LEARNING TECHNIQUES FOR LOW-DATA TRAINING [PDF]
Journal of Engineering Science (Chişinău), 2020 In the age of big data and machine learning the costs to turn the data into fuel for the algorithms is prohibitively high. Organizations that can train better models with fewer annotation efforts will have a competitive edge.
BURLACU, Alexandru
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International Journal of Applied Earth Observations and Geoinformation, 2022 Semantic segmentation of large-scale mobile laser scanning (MLS) point clouds is essential for urban scene understanding. However, most of the existing semantic segmentation methods require a large quantity of labeled data, which are labor-intensive and ...
Xiangda Lei +7 more
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Agriculture, 2020 Rice is one of the most important staple food sources worldwide. Effective and cheap monitoring of rice planting areas is demanded by many developing countries.
Mo Wang, Jing Wang, Li Chen
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