ComS2T: A complementary spatiotemporal learning system for data-adaptive
  model evolution
Zhengyang Zhou Qihe Huang Binwu Wang Jianpeng Hou Kuo Yang Yuxuan Liang Yang Wang
Spatiotemporal (ST) learning has become a crucial technique to enable smart
cities and sustainable urban development. Current ST learning models capture
the heterogeneity via various spatial convolution and temporal evolution
blocks. However, rapid urbanization leads to fluctuating distributions in urban
data and city structures over short periods, resulting in existing methods
suffering generalization and data adaptation issues. Despite efforts, existing
methods fail to deal with newly arrived observations and those methods with
generalization capacity are limited in repeated training. Motivated by
complementary learning in neuroscience, we introduce a prompt-based
complementary spatiotemporal learning termed ComS2T, to empower the evolution
of models for data adaptation. ComS2T partitions the neural architecture into a
stable neocortex for consolidating historical memory and a dynamic hippocampus
for new knowledge update. We first disentangle two disjoint structures into
stable and dynamic weights, and then train spatial and temporal prompts by
characterizing distribution of main observations to enable prompts adaptive to
new data. This data-adaptive prompt mechanism, combined with a two-stage
training process, facilitates fine-tuning of the neural architecture
conditioned on prompts, thereby enabling efficient adaptation during testing.
Extensive experiments validate the efficacy of ComS2T in adapting to various
spatiotemporal out-of-distribution scenarios while maintaining efficient
inference capabilities.