Importance inversion transfer identifies shared principles for cross-domain learning

TL;DR

This paper introduces a novel framework called Explainable Cross-Domain Transfer Learning (X-CDTL) that identifies shared structural principles across diverse scientific domains to improve knowledge transfer, especially under data scarcity and noise.

Contribution

It formalizes the X-CDTL framework and the Importance Inversion Transfer (IIT) mechanism, emphasizing domain-invariant structural anchors for cross-domain learning.

Findings

Models guided by these principles show a 56% improvement in decision stability under noise.

The framework unifies network science and explainable AI to identify structural invariants.

Results suggest a shared organizational signature across heterogeneous domains.

Abstract

The capacity to transfer knowledge across scientific domains relies on shared organizational principles. However, existing transfer-learning methodologies often fail to bridge radically heterogeneous systems, particularly under severe data scarcity or stochastic noise. This study formalizes Explainable Cross-Domain Transfer Learning (X-CDTL), a framework unifying network science and explainable artificial intelligence to identify structural invariants that generalize across biological, linguistic, molecular, and social networks. By introducing the Importance Inversion Transfer (IIT) mechanism, the framework prioritizes domain-invariant structural anchors over idiosyncratic, highly discriminative features. In anomaly detection tasks, models guided by these principles achieve significant performance gains - exhibiting a 56% relative improvement in decision stability under extreme noise - over traditional baselines. These results provide evidence for a shared organizational signature across heterogeneous domains, establishing a principled paradigm for cross-disciplinary knowledge propagation. By shifting from opaque latent representations to explicit structural laws, this work advances machine learning as a robust engine for scientific discovery.