Encoder Decoder Framework

The encoder-decoder framework is a neural network architecture used for sequence-to-sequence tasks, aiming to transform an input sequence (encoded) into a desired output sequence (decoded). Current research focuses on improving these frameworks through various techniques, including incorporating attention mechanisms, utilizing non-autoregressive decoding for faster inference, and integrating graph neural networks or other specialized modules to enhance feature extraction and context understanding. This versatile framework finds applications across diverse fields, from natural language processing (e.g., machine translation, summarization, question generation) and image processing (e.g., image captioning, medical image analysis) to robotics and control systems, significantly impacting both scientific understanding and practical applications.