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What is Cost-Effective Active Learning Framework about?

This paper presents a novel active learning framework called Cost-Effective Active Learning (CEAL) for deep image classification, which aims to reduce the reliance on large amounts of labeled training data. CEAL improves existing active learning methods by incorporating deep convolutional neural networks and a cost-effective sample selection strategy that utilizes both high confidence and uncertain samples for training. Extensive experiments demonstrate that CEAL achieves superior classification performance

Author

Nirmal

Language

EN