Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries Abstract: With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting ...

The Long Short-Term Memory (LSTM) neural network is well-suited to model this type of problem because it can learn long-term dependencies in the data. To make sequence-to-sequence predictions using a ...

Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture and attention mechanism. nlp natural-language-processing ...

This paper provides a way to improve video captioning by integrating the feature extraction capabilities of the VGG-16 Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) based ...

LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder. About the dataset. The ...

Now, let’s have a look at the architecture of the encoder-decoder model. Image source. The above image is a representation of the architecture of an encoder-decoder model. Where x is input for the ...

Kao, I., Zhou, Y., Chang, L., & Chang, F. (2020). Exploring a Long Short-Term Memory Based Encoder-Decoder Framework for Multi-Step-Ahead Flood Forecasting. Journal of ... This good performance of ...