Visual-frame prediction is a pixel-dense prediction task that infers future
frames from past frames. Lacking of appearance details, low prediction accuracy
and high computational overhead are still major problems with current models or
methods. In this paper, we propose a novel neural network model inspired by the
well-known predictive coding theory to deal with the problems. Predictive
coding provides an interesting and reliable computational framework, which will
be combined with other theories such as the cerebral cortex at different level
oscillates at different frequencies, to design an efficient and reliable
predictive network model for visual-frame prediction. Specifically, the model
is composed of a series of recurrent and convolutional units forming the
top-down and bottom-up streams, respectively. The update frequency of neural
units on each of the layer decreases with the increasing of network levels,
which results in neurons of higher-level can capture information in longer time
dimensions. According to the experimental results, this model shows better
compactness and comparable predictive performance with existing works, implying
lower computational cost and higher prediction accuracy. Code is available at
this https URL.

本文介绍了一种新型的神经网络模型，它受到了流行的预测编码理论的启发，并结合了不同层次脑皮层以不同的频率振荡的理论。模型由一系列循环和卷积单元组成，上下两层流形式。该模型表现出更好的紧凑度和与现有作品相当的预测性能，表明较低的计算成本和更高的预测准确性。