Sustainability & AI
Transfer learning
Adapting a model trained on one task to a related task, reducing the data and compute needed for new applications.
Definition
Reusing a model trained on one task or domain as the starting point for another, so that less data and computation are needed for the new task.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
A model pre-trained on broad data — images, text, spectra — already encodes general patterns; fine-tuning adapts them to a specific problem. Pan and Yang's 2010 survey systematised the field. Transfer learning is why small teams can build strong tools on top of large pre-trained models.
How it is used
In environmental work it is ubiquitous: adapting general vision models to a particular ecosystem's camera-trap images, or general language models to sustainability-report analysis, without training from scratch.
Why it matters
Transfer learning is the main mechanism by which expensive, centralised model training propagates capability — and its errors — into thousands of downstream uses. Efficiency gains and concentration of influence travel together.
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