Decoderbaserte

Decoder-based refers to systems or methods that rely on a decoder component to produce outputs from encoded inputs or latent representations. In information theory, a decoder reconstructs original data from a compressed or encoded representation, often in conjunction with an encoder.

In machine learning, decoder-based architectures are common in neural networks. Two main varieties exist: encoder-decoder models, where an encoder maps input to a latent representation and a decoder generates the target sequence or structure; and decoder-only models, also called autoregressive models, where generation proceeds strictly from previously produced tokens without a separate encoder.

Applications span multiple domains. In natural language processing, encoder-decoder frameworks are used for tasks like machine translation, text summarization, and speech recognition; decoder-only models are widely used for language modeling and text generation. In vision and multimodal tasks, decoders reconstruct or generate captions, inventories, or structured outputs from encoded representations. In data compression and communications, decoders reconstruct transmitted or stored data from encoded streams. In these contexts, the decoder’s capacity and its training interact closely with the encoder or latent space to determine output quality.

Advantages of decoder-based approaches include the ability to produce fluent, target-specific outputs and to handle variable-length results. Challenges include reliance on the quality of the encoding, exposure bias and error propagation in autoregressive decoding, and the need for substantial data and compute for training large decoders.

Related topics include encoder-decoder architectures, autoregressive models, data compression, and information theory.