Multi Decoder

Multi-decoder architectures employ multiple decoder networks alongside a shared encoder to address diverse machine learning tasks. Current research focuses on improving prediction accuracy and uncertainty quantification through techniques like ensemble learning and variance regularization, as well as optimizing the interaction between streaming and non-streaming models. These advancements are impacting fields ranging from scientific visualization and speech recognition to table extraction and medical image analysis, enabling more robust and efficient solutions. The ability to generate multiple plausible outputs and quantify uncertainty is a key benefit, enhancing the reliability and trustworthiness of model predictions.