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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes Improve & Prune (I&P), a method that integrates magnitude pruning into active learning retraining cycles to discover sparse neural networks (winning tickets) without additional computational cost. The authors report that sparse models matching dense-network accuracy at up to 95% sparsity were obtained across image classification tasks, but the work has not undergone peer review and lacks quantitative validation metrics, statistical significance testing, and comparison to alternative pruning or active learning approaches.
Empirical comparative study; multiple datasets and architectures. Image classification datasets; architectures unspecified in abstract.. Intervention: Improve & Prune (I&P): magnitude pruning integrated into active learning retraining cycles.. Compared with: Dense (unpruned) neural networks; specific active learning or pruning baselines not stated in abstract..
I&P yields sparse models matching dense-network accuracy at sparsities up to 95% across multiple image classification datasets Method integrates magnitude pruning into active learning retraining cycles at stated 'practically no additional cost' Results demonstrated across multiple acquisition functions and architecture families including active fine-tuning scenarios
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A preprint proposing a novel algorithmic integration without peer review, demonstrating feasibility on image classification but lacking independent validation, clinical application, or comparison to established baselines beyond accuracy matching.
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The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initialization, match the accuracy of the full dense network. The predominant method for discovering such tickets, iterative magnitude pruning, alternates pruning with full retraining from scratch until convergence over many cycles. Similarly, deep active learning also retrains a model from scratch after each acquisition round as new labels become available. Despite this shared reliance on iterative retraining with a substantial computational overhead, the two paradigms have been studied separately. We observe that the iterative training loop inherent to pool-based active learning already provides the exact computational structure that iterative magnitude pruning exploits, and propose Improve & Prune (I&P), a method that integrates magnitude pruning into each active learning retraining cycle at practically no additional cost. This raises a key empirical question: can iterative magnitude pruning produce winning tickets under the non-stationary data regime of active learning? We investigate this question across multiple acquisition functions, architecture families, and image classification datasets, including an active fine-tuning scenario. Our results demonstrate that I&P yields sparse, deployable models at each active learning iteration. Those match the accuracy of their dense counterparts at sparsities up to 95%, effectively obtaining winning tickets as a byproduct of the active learning pipeline. These per-iteration sparse models can address two computational bottlenecks - per-round model retraining and acquisition scoring over the unlabeled pool - that currently prevent the practical adoption of DAL on large architectures and large unlabeled pools.
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