  Spatiotemporal sequence prediction is an important problem in deep learning.
We study next-frame(s) video prediction using a deep-learning-based predictive
coding framework that uses convolutional, long short-term memory (convLSTM)
modules. We introduce a novel reduced-gate convolutional LSTM(rgcLSTM)
architecture that requires a significantly lower parameter budget than a
comparable convLSTM. By using a single multi-function gate, our reduced-gate
model achieves equal or better next-frame(s) prediction accuracy than the
original convolutional LSTM while using a smaller parameter budget, thereby
reducing training time and memory requirements. We tested our reduced gate
modules within a predictive coding architecture on the moving MNIST and KITTI
datasets. We found that our reduced-gate model has a significant reduction of
approximately 40 percent of the total number of training parameters and a 25
percent reduction in elapsed training time in comparison with the standard
convolutional LSTM model. The performance accuracy of the new model was also
improved. This makes our model more attractive for hardware implementation,
especially on small devices. We also explored a space of twenty different gated
architectures to get insight into how our rgcLSTM fit into that space.
