title: Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction
abstract: 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. Our
  reduced-gate model achieves better next-frame prediction accuracy than the original
  convolutional LSTM while using a smaller parameter budget, thereby reducing
  training time. 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 training time in comparison with the standard
  LSTM model which makes it attractive for hardware implementation especially
  on small devices.
authors: Nelly Elsayed Anthony S. Maida Magdy Bayoumi
hIndex: Magdy Bayoumi 39