DMPCN: Dynamic Modulated Predictive Coding Network with Hybrid Feedback Representations
This work addresses performance bottlenecks in predictive coding networks for computer vision tasks, offering incremental improvements in handling diverse input data.
The paper tackled limitations in predictive coding networks by introducing a hybrid feedback mechanism with dynamic modulation and a tailored loss function, resulting in faster convergence and higher predictive accuracy on CIFAR-10, CIFAR-100, MNIST, and FashionMNIST datasets.
Traditional predictive coding networks, inspired by theories of brain function, consistently achieve promising results across various domains, extending their influence into the field of computer vision. However, the performance of the predictive coding networks is limited by their error feedback mechanism, which traditionally employs either local or global recurrent updates, leading to suboptimal performance in processing both local and broader details simultaneously. In addition, traditional predictive coding networks face difficulties in dynamically adjusting to the complexity and context of varying input data, which is crucial for achieving high levels of performance in diverse scenarios. Furthermore, there is a gap in the development and application of specific loss functions that could more effectively guide the model towards optimal performance. To deal with these issues, this paper introduces a hybrid prediction error feedback mechanism with dynamic modulation for deep predictive coding networks by effectively combining global contexts and local details while adjusting feedback based on input complexity. Additionally, we present a loss function tailored to this framework to improve accuracy by focusing on precise prediction error minimization. Experimental results demonstrate the superiority of our model over other approaches, showcasing faster convergence and higher predictive accuracy in CIFAR-10, CIFAR-100, MNIST, and FashionMNIST datasets.