arXiv:2605.23198v1 Announce Type: new Abstract: Dataset pruning reduces the storage and training costs of deep learning by selecting an informative subset from a large dataset. However, most existing pruning methods require fully labeled data, which limits their applicability in realistic settings where unlabeled data are abundant and annotation is costly. Recent label-free pruning methods address this issue, but they rely on features from pretrained models to estimate example difficulty. This dependence can be unreliable when the target dataset differs substantially from the pretraining distr
The proliferation of massive datasets and the high cost of manual annotation for deep learning models are driving innovations in label-efficient data handling.
Efficient dataset pruning methods that reduce reliance on fully labeled data are crucial for scaling AI development, especially in domains with scarce or expensive annotations.
This research introduces a method for dataset pruning that functions with unlabeled or partially labeled data, potentially democratizing access to large data-driven AI systems.
- · AI developers
- · Organizations with large unlabeled datasets
- · Deep learning research
- · Data annotation services
- · Inefficient dataset management practices
Reduced computational and financial costs associated with deep learning model training.
Faster iteration and deployment of AI models across various industries due to more accessible data preparation.
An acceleration in AI innovation, particularly in fields where data labeling is a significant bottleneck, potentially leading to new applications and markets.
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Read at arXiv cs.LG