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

arXiv: https://arxiv.org/abs/2504.08364 · HTML: https://mlsanity.com/paper/2504.08364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [Online Coreset Selection for Rehearsal-based Continual Learning](https://mlsanity.com/paper/2106.01085/) (2021)
- [PUMA: Performance Unchanged Model Augmentation for Training Data Removal](https://mlsanity.com/paper/2203.00846/) (2022)
- [Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification](https://mlsanity.com/paper/2406.05677/) (2024)
- [Dataset balancing can hurt model performance](https://mlsanity.com/paper/2307.00079/) (2023)
- [A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption](https://mlsanity.com/paper/2409.07114/) (2024)
- [Advancing On-Device Neural Network Training with TinyPropv2: Dynamic, Sparse, and Efficient Backpropagation](https://mlsanity.com/paper/2409.07109/) (2024)

## Built on by (most cited first, 1 of 1)

- [Toward a Modular Architecture for Embedded AI Agent Systems at the Edge](https://mlsanity.com/paper/2606.02862/) (2026)

## Abstract (arXiv)

Selecting data points for model training is critical in machine learning. Effective selection methods can reduce the labeling effort, optimize on-device training for embedded systems with limited data storage, and enhance the model performance. This paper introduces a novel algorithm that uses Grad-CAM to make online decisions about retaining or discarding data points. Optimized for embedded devices, the algorithm computes a unique DRIP Score to quantify the importance of each data point. This enables dynamic decision-making on whether a data point should be stored for potential retraining or discarded without compromising model performance. Experimental evaluations on four benchmark datasets demonstrate that our approach can match or even surpass the accuracy of models trained on the entire dataset, all while achieving storage savings of up to 39%. To our knowledge, this is the first algorithm that makes online decisions about data point retention without requiring access to the entire dataset.