SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Journal:
arXiv
Published Date:
May 21, 2025
Abstract
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.
