Encoder-Decoder

The encoder-decoder architecture is a foundational concept in neural networks, primarily utilized in tasks requiring sequence-to-sequence transformations, such as machine translation, image captioning, and speech recognition. This architecture is designed to effectively process and generate sequences of data, adhering to a structured interaction between its two main components: the encoder and the decoder.

The encoder serves as the initial phase of the architecture, tasked with converting an input sequence into a fixed-size context vector—or, more generally, a set of hidden states. The process begins with the input being fed into a neural network, most commonly a recurrent neural network (RNN), long short-term memory network (LSTM), or gated recurrent unit (GRU). As the input sequence, typically composed of a series of tokens or features, is processed step by step, the encoder generates hidden states that encapsulate relevant information from the input. The ultimate output of the encoder is a summary of the input sequence in the form of a context vector, which retains semantic meaning and contextual relevance.

Following the encoder, the decoder receives this context vector and aims to produce an output sequence. The decoder is also usually structured as an RNN, LSTM, or GRU, enabling it to handle sequential data in a similar vein to the encoder. Importantly, the decoder generates its output one step at a time, leveraging the context vector as an initial condition and previous outputs to inform subsequent predictions. This sequential generation facilitates a dynamic interaction with the model's hidden state, effectively maintaining memory of previously produced tokens, which enhances coherence in the output sequence.

Additionally, encoder-decoder models often incorporate attention mechanisms, which allow the decoder to selectively focus on different parts of the input sequence while generating its output. This attention mechanism addresses limitations related to fixed-length context vectors, enhancing the model's ability to handle long-range dependencies within the data. By assigning different weights to various parts of the input, the model can dynamically access contextually relevant information to the current output generation step.