arXiv:2605.23028v1 Announce Type: new Abstract: Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for domain expertise. Expanding datasets with additional sources is a common response to limited data, yet this practice does not always improve downstream performance and can sometimes lead to a loss of performance, known as negative transfer. We propose RADAR, a simple, geometrically grounded metric for estimating cross-domain transferability in foundation models. RADAR analyzes the layer-wise evolution o
The proliferation of foundation models and the increasing need for efficient transfer learning beyond narrow datasets necessitates new methods for evaluating cross-domain transferability.
This metric provides a robust way to assess model generalization across diverse datasets, which is critical for optimizing resource allocation in AI development and deployment, especially in data-constrained environments.
AI developers can more effectively and predictably expand datasets without risking negative transfer, leading to more efficient model training and potentially faster deployment cycles.
- · AI model developers
- · Foundation model companies
- · Sectors with limited data
- · AI research institutions
- · Inefficient AI data acquisition strategies
- · Trial-and-error model integration
Improved efficiency in training foundation models by better predicting transfer learning outcomes.
Faster development and deployment of specialized AI applications across various industries due to predictable model adaptation.
Reduced computational waste and potentially lower energy consumption for AI development as fewer erroneous training cycles are needed.
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Read at arXiv cs.LG