CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
This work addresses the challenge of enhancing prediction accuracy in real-world forecasting tasks like weather prediction, though it is incremental as it builds on existing diffusion models and Predictive Coding theory.
The authors tackled the problem of improving real-world spatiotemporal forecasting by integrating the precision weighting mechanism from cognitive science's Predictive Coding theory into diffusion probabilistic models, resulting in CogDPM outperforming existing domain-specific and general deep learning models on precipitation and wind datasets.
Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of the visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theory, emulating its two core mechanisms: Correcting predictions from residuals and hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks and ignore the Precision Weighting mechanism of PC theory. The precision weighting mechanism posits that the brain allocates more attention to signals with lower precision, contributing to the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM), which demonstrate the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models and weight the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights effectively estimate the data predictability. We apply CogDPM to real-world prediction tasks using the United Kindom precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models by providing more proficient forecasting.