Encoder-Decoder
An Encoder-Decoder is a type of neural network architecture commonly used in tasks that involve sequence-to-sequence processing, such as language translation, text summarization, and image captioning. This architecture is particularly effective for handling variable-length input and output sequences.
The architecture comprises two main components: the encoder and the decoder. The encoder processes the input data and converts it into a fixed-size context vector that encapsulates the information of the input sequence. This vector serves as a summary of the input, capturing its essential features. Typically, the encoder consists of layers of recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformers.
Once the input is encoded, the decoder takes the context vector and generates the output sequence one element at a time. The decoder can also be built using RNNs, CNNs, or transformers and uses the previously generated output as part of its input to predict the next element in the sequence. This process continues until a special end-of-sequence token is generated, indicating that the output is complete.
One significant advancement in Encoder-Decoder architectures is the introduction of attention mechanisms. Attention allows the decoder to focus on specific parts of the input sequence when generating each element of the output, improving the model’s performance, especially in long sequences. The combination of encoders and decoders with attention mechanisms has led to state-of-the-art results in various natural language processing tasks.