New method integrates pruning into active learning to find sparse models

Researchers have developed a new method called Improve & Prune (I&P) that integrates magnitude pruning into active learning retraining cycles. This approach aims to discover sparse subnetworks, or "winning tickets," within active learning pipelines without significant additional computational cost. The findings suggest that I&P can produce deployable sparse models at each active learning iteration, achieving accuracy comparable to dense models with up to 95% sparsity. This method addresses computational bottlenecks in retraining and acquisition scoring, potentially enabling the practical adoption of active learning on larger models and datasets. AI

IMPACT This research could enable more efficient training and deployment of sparse models in active learning scenarios, potentially reducing computational costs and improving scalability.

RANK_REASON The cluster contains an academic paper detailing a new method for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• active learning
• arXiv
• Benedikt Tscheschner
• cs.CV
• cs.LG
• Hugging Face
• Improve & Prune (I&P)
• Iterative Magnitude Pruning
• lottery ticket hypothesis

• paper
• model release

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