REDUCED|GATE CONVOLUTIONAL LSTM DESIGN USING ...

Reduced-Gate Convolutional LSTM Using Predictive Coding for ...

By using a single multi-function gate, our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original ...

Reduced-Gate Convolutional LSTM Architecture for Next-Frame ...

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 ...

Reduced‐gate convolutional long short‐term memory using ...

The LSTM network, first introduced by Hochreiter and Schmidhuber,14 was the first gated RNN recurrent unit design to mitigate the vanishing and/ ...

Reduced‐gate convolutional long short‐term memory using ...

We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional LSTM (convLSTM) modules.

REDUCED-GATE CONVOLUTIONAL LSTM DESIGN USING ...

We study next-frame video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) mod-.

(PDF) Reduced-Gate Convolutional LSTM Using Predictive Coding ...

Our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller ...

NellyElsayed/rgcLSTM: reduced-gate convolutional LSTM ... - GitHub

We introduced a novel reduced-gated convolutional LSTM (rgcLSTM) which has the same performance as convolutional LSTM (cLSTM) in RGB videos and outperforms the ...

Reduced‐gate convolutional long short‐term memory using ...

Elsayed, N, Maida, A, Bayoumi, M. Reduced‐gate convolutional LSTM architecture for next‐frame video prediction using predictive coding. Paper presented at: ...

Reduced-Gate Convolutional LSTM Design Using Predictive Coding ...

The reduced-gate model achieves better next-frame prediction accuracy than the original convolutional LSTM while using a smaller parameter ...

Reduced-Gate Convolutional LSTM Architecture for Next-Frame ...

use a novel, reduced-gate convolutional LSTM (rgcLSTM). ... [5] showed how to build a predictive coding model using a convolutional LSTM architec- ... in the design ...

Reduced-Gate Convolutional LSTM Architecture for Next-Frame ...

Reduced-Gate Convolutional LSTM Architecture for Next-Frame Video Prediction Using Predictive Coding ... To read the full-text of this research, you can request a ...

Data augmentation for Convolutional LSTM based brain computer ...

The EEG data were firstly transformed using the Morlet wavelet and then fed to an originally designed Convolutional Neural Network (CNN) with long short term ...

Slim LSTMs: parameter-reductions within gating signals | Connected ...

Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction. Nelly Elsayed, A. Maida, M. Bayoumi. 2018. How well can we ...

Convolutional LSTM Network: A Machine Learning Approach for ...

LSTM (FC-LSTM) to have convolutional ... One advantage of using the memory cell and gates to control information flow ... This structure is also similar to the LSTM ...

Novel attention-based convolutional autoencoder and ConvLSTM ...

To construct reduced-order models, we propose a convolutional autoencoder and a convolutional LSTM (CAE-ConvLSTM). Using convolutional LSTMs ...

Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly ...

In our approach, we proposed two strategies: We designed the Multi-Scale Convolutional LSTM model and performed the initial training on one ...

Video Frame Prediction using ConvLSTM Network in PyTorch

Also the no. of parameters hugely reduces, compared to a fully LSTM based network, completely analogous to how the no. of parameters in a ...

Attention in Convolutional LSTM for Gesture Recognition

Several variants of ConvLSTM are evaluated: (a) Removing the convolutional structures of the three gates in ConvLSTM, (b) Applying the attention mechanism ...

What is Convolutional Long Short-Term Memory (ConvLSTM)

The Forget Gate is responsible for deciding what information to forget from the memory cell of the LSTM model. It takes in the input from the ...

Jewelry rock discrimination as interpretable data using laser ...

Convolutional neural network (CNN) and long short-term memory (LSTM) networks are widely used techniques in deep learning algorithms. The main ...