Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification
نویسندگان
چکیده
منابع مشابه
Active Learning with Ensembles for Image Classification
In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant reduction in required expert involvement ...
متن کاملActive Learning with Ensembles for Image Classification
In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant reduction in required expert involvement ...
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Obtaining labeled data for supervised classification of remotely sensed imagery is expensive and time consuming. Further, manual selection of the training set is often subjective and tends to induce redundancy into the supervised classifier, thus considerably slowing the training phase. Active learning (AL) integrates data acquisition with the classifier design by ranking the unlabeled data to ...
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This paper shows how a text classifier’s need for labeled training documents can be reduced by employing a large pool of unlabeled documents. We modify the Query-by-Committee (QBC) method of active learning to use the unlabeled pool by explicitly estimating document density when selecting examples for labeling. Then active learning is combined with Expectation-Maximization in order to “fill in”...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3038676