<ul><li>Effective selection methods for model training can reduce labeling effort, optimize on-device training, and enhance model performance.</li><li>A novel algorithm using Grad-CAM is introduced for online decision-making on data point retention or discarding.</li><li>The algorithm computes a unique DRIP Score to quantify the importance of each data point.</li><li>Experimental evaluations show that the approach achieves storage savings of up to 39% while maintaining or surpassing model accuracy.</li></ul>

DRIP: DRop unImportant data Points -- Enhancing Machine Learning Efficiency with Grad-CAM-Based Real-Time Data Prioritization for On-Device Training

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