Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction

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Summary: Authors present a new LSTM architecture, i.e. reduced gate convolutional LSTM. Authors use only one trainable gate, i.e. the forget gate which leads to less trainable parameters. They demonstrate the superiority of ti in two datasets, the moving MNIST and KITTI. Their results show that their architecture performs better than others in more accurately predicting next-frame. The paper is clearly written and the evaluation is based on other proposed similar architectures. For the moving MNIST they compared their model against the vanilla convLSTM with three gates and no peephole connections, based on previous work which has shown that it exceeds the accuracy performance of other LSTM based approaches.