SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
This work addresses the problem of cross-domain adaptation in spatiotemporal learning for scientific and social planning applications, offering a novel paradigm for knowledge transfer.
The paper tackles the limited transferability of spatiotemporal models across domains by proposing SynEVO, a neuro-inspired framework that enables collective intelligence and model evolution, resulting in up to 42% improvement in generalization capacity under cross-domain scenarios.
Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a specific source data that leads to limited transferability among sources, where even correlated tasks requires new design and training. The key towards increasing cross-domain knowledge is to enable collective intelligence and model evolution. In this paper, inspired by neuroscience theories, we theoretically derive the increased information boundary via learning cross-domain collective intelligence and propose a Synaptic EVOlutional spatiotemporal network, SynEVO, where SynEVO breaks the model independence and enables cross-domain knowledge to be shared and aggregated. Specifically, we first re-order the sample groups to imitate the human curriculum learning, and devise two complementary learners, elastic common container and task-independent extractor to allow model growth and task-wise commonality and personality disentanglement. Then an adaptive dynamic coupler with a new difference metric determines whether the new sample group should be incorporated into common container to achieve model evolution under various domains. Experiments show that SynEVO improves the generalization capacity by at most 42% under cross-domain scenarios and SynEVO provides a paradigm of NeuroAI for knowledge transfer and adaptation.