What are the differences between seq2seq and encoder-decoder architectures?

Question

I've read many tutorials online that use both words interchangeably. When I search and find that they are the same, why not just use one word since they have the same definition?

Answer 1

They are not the same, but they can overlap.

An encoder-decoder architecture is composed of an encoder (which compresses the input) and a decoder (which decompresses the compressed input).

A sequence-to-sequence (or sequence transduction) model is a model that converts sequences to other sequences. The most obvious example are models for machine translation, where sequences are sentences in 2 different languages (e.g. English and French). See e.g. NMT or the transformer. These models also use an encoder-decoder architecture.

The variational autoencoder (VAE) uses an encoder-decoder architecture, but it not usually used to convert sequences to other sequences.

So, in conclusion, encoder-decoder architectures are not just used for sequence transduction tasks, and sequence-to-sequence models may not use encoder-decoder architectures, although famous models like the original transformer do.

Answer 2

Yes, you may have read tutorials or texts using interchangeably because of close relationships, but actually, there is a subtle distinction.

Encoder-Decoder: It contains two main components Encoder and Decoder which take information to create context. The decoder takes context and generates the output. The key point is it can be used in various applications like NLP generation, Machine translation, Image Captioning etc... It Supports CNN and RNN or similar (GRU, LSTM, etc...).

Seq2Seq Model: Sequence to Sequence is a type of model that is specifically designed to handle sequences. It is mainly used in tasks where input and output are both in sequences. So, we can say that the seq2seq model also uses the encoder and decoder-based network specific to the nature of data and task. It only supports RNN, LSTM, or GRU.

So, we can say that seq2seq is an encoder-decoder-based architecture while the encoder and decoder model represents a wider range of applications and data.