Importance inversion transfer identifies shared principles for cross-domain learning
This work addresses the challenge of cross-domain learning for scientific applications, offering a novel paradigm that is not incremental but shifts from opaque to explicit structural principles.
The study tackled the problem of transferring knowledge across heterogeneous domains under data scarcity and noise by formalizing Explainable Cross-Domain Transfer Learning (X-CDTL) with an Importance Inversion Transfer (IIT) mechanism, resulting in a 56% relative improvement in decision stability for anomaly detection tasks.
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.