A decoder is a component of an AI model, particularly in transformer-based architectures, that generates output sequences by interpreting encoded representations. It is commonly used in tasks such as text generation, translation, and summarization, where it converts abstract representations into human-readable formats.
How It Works:
- Input from Encoder: The decoder receives a structured representation of the input data, typically generated by an encoder.
- Step-by-Step Generation: Using attention mechanisms and context from previous outputs, the decoder predicts the next token or element in a sequence.
- Final Output: The process continues until a complete output sequence, such as a translated sentence, is produced.
Applications:
- Machine Translation: Decodes text representations into a target language.
- Speech Recognition: Converts audio features into textual transcriptions.
- Image Captioning: Generates descriptive text for visual inputs.
Why It Matters:
Decoders are essential for tasks where AI systems must produce coherent and meaningful outputs. By bridging abstract data and user-friendly results, decoders play a critical role in AI’s real-world applications.
