Reduced-Gate Convolutional LSTM Architecture for Next-Frame Video Prediction Using Predictive Coding
Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi · 2019
Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame 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 architecture that achieves better next-frame 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 gray-scale and RGB video datasets. We found that our reduced-gate model has a significant reduction of approximately 40 percent of the total number of training parameters and training time in comparison with the standard LSTM model which makes it attractive for hardware implementation especially on small devices.