In neural networks, particularly in sequence-to-sequence models, an encoder is responsible for processing input data and compressing it into a fixed-size representation, often referred to as a context or latent vector. This representation contains the essential information required to predict the output.
A decoder, on the other hand, takes this compressed information and generates the corresponding output, such as a translation in a language translation task or the next word in a text generation task. The encoder-decoder architecture is used in models like transformers, LSTMs, and seq2seq models.
While the encoder focuses on capturing the input’s essential features, the decoder generates the output based on the encoded information. This structure is fundamental for tasks involving sequential data, such as machine translation or summarization.