Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation

• URL: http://arxiv.org/abs/2402.11922v3
• Date: Mon, 25 Mar 2024 11:39:57 GMT
• Title: Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
• Authors: Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li,
• Abstract summary: We propose a novel generative pre-training framework, GPD, for intricate few-shot learning with urban knowledge transfer. We recast a generative diffusion model, which generates tailored neural networks guided by prompts. GPD consistently outperforms state-of-the-art baselines on datasets for tasks such as traffic speed prediction and crowd flow prediction.
• Score: 25.916891462152044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPD, for spatio-temporal few-shot learning with urban knowledge transfer. Unlike conventional approaches that heavily rely on common feature extraction or intricate few-shot learning designs, our solution takes a novel approach by performing generative pre-training on a collection of neural network parameters optimized with data from source cities. We recast spatio-temporal few-shot learning as pre-training a generative diffusion model, which generates tailored neural networks guided by prompts, allowing for adaptability to diverse data distributions and city-specific characteristics. GPD employs a Transformer-based denoising diffusion model, which is model-agnostic to integrate with powerful spatio-temporal neural networks. By addressing challenges arising from data gaps and the complexity of generalizing knowledge across cities, our framework consistently outperforms state-of-the-art baselines on multiple real-world datasets for tasks such as traffic speed prediction and crowd flow prediction. The implementation of our approach is available: https://github.com/tsinghua-fib-lab/GPD.

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