The Encoder-Decoder architecture is widely used in sequence-to-sequence tasks such as machine translation, text summarization, and speech recognition. However, traditional Encoder-Decoder models face ...

If they are very long, you might need a more complex encoder-decoder architecture that can handle the long-term dependencies and avoid information loss. For example, you might use an attention ...

Neural Machine Translation using LSTMs and Attention mechanism. Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture ...

Two of the main families of neural network architecture are encoder-decoder architecture and the Generative Adversarial Network (GAN). In 2015, Sequence to Sequence Learning with Neural Network became ...

This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder–decoder network to segment out ...

Modern large language models (LLMs) have excellent performance on code reading and generation tasks, allowing more people to enter the once-mysterious field of computer programming ... often adopt an ...

Abstract: Describing the contents of an image without human intervention is a complex task. Computer Vision and Natural Language Processing are widely used for tackling this problem. It requires an ...

Over the last five years, researchers have started rethinking compression as a computer vision problem ... can be re-trained), the same encoder or decoder architecture can be specialised for ...