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?
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2 Answers
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5They 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.
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- $\begingroup$What about GANs ? Are they seq2seq or encoder-decoder ?$\endgroup$– user78615CommentedDec 7, 2023 at 12:29
- 1$\begingroup$@user I don't think GANs have an encoder-decoder architecture, but I honestly don't remember the details. They have a generator and a discriminator (which are trained in some game theory fashion), but I don't remember if they use this ED architecture. Afaik, GANs are mostly used for image generation tasks, so not for sequence transduction tasks, but I don't exclude they may also be used for these tasks. I'd need to check that (i.e. read the paper again). Maybe later.$\endgroup$– nbroCommentedDec 7, 2023 at 13:48
- 1$\begingroup$Are there any seq2seq problems that don't use encoder-decoder architectures ?$\endgroup$– user78615CommentedDec 7, 2023 at 17:11
- 1$\begingroup$I can't think of a specific model right now, but I am sure there are (maybe check hidden Markov models or even just simple RNNs). But one reason why the encoder-decoder is useful in the case of sequence modeling is because the input and output sequences may have different sizes (e.g. number of words or characters), so you cannot just map one token to another token. With the ED, you just let the model learn a latent representation (which is what the encoder does) and then the decoder tries output sequence conditioned on the latent vector. Or something like that.$\endgroup$– nbroCommentedDec 8, 2023 at 1:36
- $\begingroup$In conclusion, the core difference is that the encoder-decoder model is defined by its architecture and a sequence-to-sequence model by the task it solves.$\endgroup$– Green 绿色CommentedNov 22, 2024 at 6:58
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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.
