An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process
arXiv:2603.13584v2 Announce Type: replace-cross Abstract: Deep learning has achieved recognition for its impact within natural sciences, yet the prohibitive financial and technical cost of training models from scratch inhibit adoption. Following software engineering community guidance, natural scientists are reusing pre-trained deep learning models (PTMs) to amortize these costs. While prior works recommend PTM reuse patterns, we present the first empirical study of PTM reuse patterns in the natural sciences, quantifying the utilization and impact of PTM reuse within the scientific process acr
Deep learning models are increasingly prevalent in scientific research, and the practical challenges of their implementation (cost, expertise) are becoming a critical focus for broader adoption.
The widespread reuse of pre-trained deep learning models can significantly democratize advanced AI capabilities within scientific research, accelerating discovery and reducing barriers to entry for institutions and researchers with fewer resources.
This empirical study validates and quantifies the effectiveness of reusing pre-trained deep learning models in natural sciences, which could lead to standardized best practices and further integration of AI without bespoke model training.
- · Natural Science Researchers
- · Smaller Research Institutions
- · AI Platform Providers
- · Open Source AI Initiatives
- · Researchers reliant on custom model training
- · AI Consulting Firms (for bespoke model development)
- · Hardware providers focused solely on high-end training clusters
Increased adoption of deep learning across diverse scientific fields due to reduced cost and complexity.
A shift in research focus from foundational AI model development to application and fine-tuning across specific scientific problems.
New interdisciplinary scientific breakthroughs enabled by the rapid integration of advanced AI without high computational overhead.
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Read at arXiv cs.AI