Encoders and decoders are complementary components in neural networks that handle input processing and output generation, respectively. An encoder converts input data into a compact, structured representation (often called a latent vector or context), while a decoder uses this representation to reconstruct data or generate new outputs. These components are commonly paired in architectures like autoencoders (for data compression/reconstruction) and sequence-to-sequence models (for tasks like translation). For example, in machine translation, an encoder processes the source language sentence, and the decoder generates the target language sentence.
Encoders focus on distilling the input into its essential features. They typically reduce dimensionality through layers like convolutions (for images) or recurrent/attention layers (for text). In a convolutional autoencoder, the encoder might use strided convolutions to shrink an image’s spatial dimensions while increasing channel depth, capturing hierarchical patterns. In natural language processing (NLP), a transformer encoder processes text by applying self-attention to build contextual embeddings, where each token’s representation incorporates information from surrounding words. The output is a fixed-size vector (or sequence of vectors) that summarizes the input. For instance, in BERT, the encoder produces embeddings used for tasks like sentiment analysis, where the model must “understand” the input’s meaning without generating new text.
Decoders reverse this process, mapping the encoded representation back to a usable format. They often employ upsampling layers (e.g., transposed convolutions for images) or autoregressive mechanisms (e.g., LSTMs or transformer decoders for text). In an autoencoder’s decoder, transposed convolutions might reconstruct an image from the latent vector by expanding spatial dimensions. In NLP, a transformer decoder generates text token-by-token, using masked self-attention to prevent future token leakage and cross-attention to align with the encoder’s context. For example, in a translation model, the decoder starts with the encoder’s context vector and iteratively predicts each word in the target language, conditioning each step on previous outputs. Unlike encoders, decoders are designed for generation, often incorporating techniques like beam search or sampling to produce coherent sequences.
A key distinction lies in their use cases and design. Encoders excel at feature extraction and are standalone in models like BERT, while decoders drive generative tasks, as seen in GPT. In VAEs, the encoder outputs parameters of a probability distribution, and the decoder samples from it to create new data. Practically, developers implement them as separate modules in frameworks like PyTorch—for example, a bidirectional LSTM encoder paired with a attention-based LSTM decoder for translation. Their roles are distinct: encoders compress and interpret, decoders expand and create, but both rely on each other in architectures requiring end-to-end processing.