The model leverages the Inception v3 pre-trained model for feature extraction from images and an LSTM-based decoder to generate captions. The architecture follows an encoder-decoder structure ... The ...

The class to implement Decoder in the encoder-decoder architecture using "RNN"/"LSTM"/"GRU". While the code is flexible enough to support separate types of recurrent units for encoder and decoder, In ...

Additionally, the training process lacks labelled video data. To enhance the precision of the performance we have used encoder decoder architecture for semantic segmentation where VGGNet (VGG16 and ...

Attention mechanisms, especially in transformer models, have significantly enhanced the performance of encoder-decoder architectures, making them highly effective for a wide range of ...

What Is An Encoder-Decoder Architecture? An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a ...

In 2015, Sequence to Sequence Learning with Neural Network became a very popular architecture and with that the encoder-decoder architecture also became part of wide deep learning community. The paper ...

Jahangir, M.S. , You, J. , & Quilty, J.. (2022, December 12-16). Quantile-Based Encoder-Decoder Deep Learning Models for Multi-Step Ahead Hydrological Forecasting [Conference presentation]. American ...