Description

The encoder-decoder architecture is a neural network design pattern that consists of two main components: an encoder that processes and compresses input data into a fixed-length representation, and a decoder that takes this representation and generates output. This architecture is particularly useful for tasks where the input and output can have different lengths or structures. It forms the basis for many sequence-to-sequence models and has been successfully applied to various natural language processing and computer vision tasks.

Examples

• 🌐 Neural machine translation
• 📝 Text summarization
• 🖼️ Image captioning

Applications