Encoder Decoder Architecture

Encoder-decoder architectures are a fundamental class of neural networks designed to map input sequences to output sequences, achieving this through separate encoding and decoding stages. Current research focuses on improving efficiency and robustness across diverse applications, employing variations of established models like U-Net, Transformers, and ResNets, often incorporating attention mechanisms and other enhancements to handle complex data like images, audio, and graphs. These advancements are significantly impacting fields ranging from medical image analysis and speech enhancement to natural language processing and 3D modeling, enabling more accurate and efficient solutions to complex problems.