We use PyTorch to build the LSTM encoder-decoder in lstm_encoder_decoder.py. The LSTM encoder takes an input sequence and produces an encoded state (i.e., cell state and hidden state). We feed the ...

Building an Encoder-Decoder with LSTM layers for Time-Series forecasting; Understanding Encoder-Decoder Model. In machine learning, we have seen various kinds of neural networks and encoder-decoder ...

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 ...

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 ...

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 ...

There are many instances where we would like to predict how a time series will behave in the future. For example, we may be interested in forecasting web page viewership, weather conditions ...

The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions ...