Oct 1, 2024 · Encoder-decoder models are used to handle sequential data, specifically mapping input sequences to output sequences of different lengths, such as neural machine translation, text summarization, image captioning and speech recognition. In such tasks, mapping a token in the input to one in the output is often indirect.

Oct 17, 2021 · How does an Encoder-Decoder work and why use it in Deep Learning? The Encoder-Decoder is a neural network discovered in 2014 and it is still used today in many projects. It is a fundamental pillar of Deep Learning. It is found in particular in translation software.

Mar 10, 2021 · There are three main blocks in the encoder-decoder model, The Encoder will convert the input sequence into a single-dimensional vector (hidden vector). The decoder will convert the hidden...

Mar 11, 2021 · Encoder-Decoder models were originally built to solve such Seq2Seq problems. In this post, I will be using a many-to-many type problem of Neural Machine Translation (NMT) as a running...

Jan 12, 2024 · What’s Encoder-Decoder Architecture & How does it work? The encoder-decoder architecture is a deep learning architecture used in many natural language processing and computer vision applications. It consists of two main components: an encoder and a decoder.

Jun 24, 2023 · Using an encoder-decoder architecture, the model can take an input image and generate a caption that accurately describes the contents of the image. This is achieved by first encoding each...

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

Feb 13, 2025 · Encoder-Decoder models and Recurrent Neural Networks are probably the most natural way to represent text sequences. In this tutorial, we’ll learn what they are, different architectures, applications, issues we could face using them, and what are the most effective techniques to overcome those issues.

Encoder-decoder models (also known as sequence-to-sequence or seq2seq models) combine the strengths of both encoder and decoder architectures. These models use an encoder to process and understand the input sequence and a decoder to generate the output sequence.

Aug 27, 2020 · The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation.